Research-Methodology

Deductive Approach (Deductive Reasoning)

A deductive approach is concerned with “developing a hypothesis (or hypotheses) based on existing theory, and then designing a research strategy to test the hypothesis” [1]

It has been stated that “deductive means reasoning from the particular to the general. If a causal relationship or link seems to be implied by a particular theory or case example, it might be true in many cases. A deductive design might test to see if this relationship or link did obtain on more general circumstances” [2] .

Deductive approach can be explained by the means of hypotheses, which can be derived from the propositions of the theory. In other words, deductive approach is concerned with deducting conclusions from premises or propositions.

Deduction begins with an expected pattern “that is tested against observations, whereas induction begins with observations and seeks to find a pattern within them” [3] .

Advantages of Deductive Approach

Deductive approach offers the following advantages:

  • Possibility to explain causal relationships between concepts and variables
  • Possibility to measure concepts quantitatively
  • Possibility to generalize research findings to a certain extent

Alternative to deductive approach is  inductive approach.  The table below guides the choice of specific approach depending on circumstances:

Choice between deductive and inductive approaches

Deductive research approach explores a known theory or phenomenon and tests if that theory is valid in given circumstances. It has been noted that “the deductive approach follows the path of logic most closely. The reasoning starts with a theory and leads to a new hypothesis. This hypothesis is put to the test by confronting it with observations that either lead to a confirmation or a rejection of the hypothesis” [4] .

Moreover, deductive reasoning can be explained as “reasoning from the general to the particular” [5] , whereas inductive reasoning is the opposite. In other words, deductive approach involves formulation of hypotheses and their subjection to testing during the research process, while inductive studies do not deal with hypotheses in any ways.

Application of Deductive Approach (Deductive Reasoning) in Business Research

In studies with deductive approach, the researcher formulates a set of hypotheses at the start of the research. Then, relevant research methods are chosen and applied to test the hypotheses to prove them right or wrong.

Deductive Approach Deductive Reasoning

Generally, studies using deductive approach follow the following stages:

  • Deducing  hypothesis from theory.
  • Formulating  hypothesis in operational terms and proposing relationships between two specific variables
  • Testing  hypothesis with the application of relevant method(s). These are quantitative methods such as regression and correlation analysis, mean, mode and median and others.
  • Examining  the outcome of the test, and thus confirming or rejecting the theory. When analysing the outcome of tests, it is important to compare research findings with the literature review findings.
  • Modifying  theory in instances when hypothesis is not confirmed.

My e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance  contains discussions of theory and application of research approaches. The e-book also explains all stages of the  research process  starting from the  selection of the research area  to writing personal reflection. Important elements of dissertations such as  research philosophy ,   research design ,  methods of data collection ,   data analysis  and   sampling   are explained in this e-book in simple words.

John Dudovskiy

Deductive Approach (Deductive Reasoning)

[1] Wilson, J. (2010) “Essentials of Business Research: A Guide to Doing Your Research Project” SAGE Publications, p.7

[2] Gulati, PM, 2009, Research Management: Fundamental and Applied Research, Global India Publications, p.42

[3] Babbie, E. R. (2010) “The Practice of Social Research” Cengage Learning, p.52

[4] Snieder, R. & Larner, K. (2009) “The Art of Being a Scientist: A Guide for Graduate Students and their Mentors”, Cambridge University Press, p.16

[5] Pelissier, R. (2008) “Business Research Made Easy” Juta & Co., p.3

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  • How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection .

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, other interesting articles, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

  • An independent variable is something the researcher changes or controls.
  • A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

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Step 1. Ask a question

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2. Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.

Step 3. Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

4. Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

  • The relevant variables
  • The specific group being studied
  • The predicted outcome of the experiment or analysis

5. Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in  if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis . The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

  • H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
  • H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

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

  • Sampling methods
  • Simple random sampling
  • Stratified sampling
  • Cluster sampling
  • Likert scales
  • Reproducibility

 Statistics

  • Null hypothesis
  • Statistical power
  • Probability distribution
  • Effect size
  • Poisson distribution

Research bias

  • Optimism bias
  • Cognitive bias
  • Implicit bias
  • Hawthorne effect
  • Anchoring bias
  • Explicit bias

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forming a hypothesis is accomplished through deductive reasoning

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A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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1.2: The Science of Biology - Scientific Reasoning

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Learning Objectives

  • Compare and contrast theories and hypotheses

The Process of Science

Science (from the Latin scientia, meaning “knowledge”) can be defined as knowledge that covers general truths or the operation of general laws, especially when acquired and tested by the scientific method. The steps of the scientific method will be examined in detail later, but one of the most important aspects of this method is the testing of hypotheses (testable statements) by means of repeatable experiments. Although using the scientific method is inherent to science, it is inadequate in determining what science is. This is because it is relatively easy to apply the scientific method to disciplines such as physics and chemistry, but when it comes to disciplines like archaeology, paleoanthropology, psychology, and geology, the scientific method becomes less applicable as it becomes more difficult to repeat experiments.

These areas of study are still sciences, however. Consider archaeology: even though one cannot perform repeatable experiments, hypotheses may still be supported. For instance, an archaeologist can hypothesize that an ancient culture existed based on finding a piece of pottery. Further hypotheses could be made about various characteristics of this culture. These hypotheses may be found to be plausible (supported by data) and tentatively accepted, or may be falsified and rejected altogether (due to contradictions from data and other findings). A group of related hypotheses, that have not been disproven, may eventually lead to the development of a verified theory. A theory is a tested and confirmed explanation for observations or phenomena that is supported by a large body of evidence. Science may be better defined as fields of study that attempt to comprehend the nature of the universe.

Scientific Reasoning

One thing is common to all forms of science: an ultimate goal “to know.” Curiosity and inquiry are the driving forces for the development of science. Scientists seek to understand the world and the way it operates. To do this, they use two methods of logical thinking: inductive reasoning and deductive reasoning.

image

Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. This type of reasoning is common in descriptive science. A life scientist such as a biologist makes observations and records them. These data can be qualitative or quantitative and the raw data can be supplemented with drawings, pictures, photos, or videos. From many observations, the scientist can infer conclusions (inductions) based on evidence. Inductive reasoning involves formulating generalizations inferred from careful observation and the analysis of a large amount of data. Brain studies provide an example. In this type of research, many live brains are observed while people are doing a specific activity, such as viewing images of food. The part of the brain that “lights up” during this activity is then predicted to be the part controlling the response to the selected stimulus; in this case, images of food. The “lighting up” of the various areas of the brain is caused by excess absorption of radioactive sugar derivatives by active areas of the brain. The resultant increase in radioactivity is observed by a scanner. Then researchers can stimulate that part of the brain to see if similar responses result.

Deductive reasoning or deduction is the type of logic used in hypothesis-based science. In deductive reason, the pattern of thinking moves in the opposite direction as compared to inductive reasoning. Deductive reasoning is a form of logical thinking that uses a general principle or law to forecast specific results. From those general principles, a scientist can extrapolate and predict the specific results that would be valid as long as the general principles are valid. Studies in climate change can illustrate this type of reasoning. For example, scientists may predict that if the climate becomes warmer in a particular region, then the distribution of plants and animals should change. These predictions have been written and tested, and many such predicted changes have been observed, such as the modification of arable areas for agriculture correlated with changes in the average temperatures.

Both types of logical thinking are related to the two main pathways of scientific study: descriptive science and hypothesis-based science. Descriptive (or discovery) science, which is usually inductive, aims to observe, explore, and discover, while hypothesis-based science, which is usually deductive, begins with a specific question or problem and a potential answer or solution that can be tested. The boundary between these two forms of study is often blurred and most scientific endeavors combine both approaches. The fuzzy boundary becomes apparent when thinking about how easily observation can lead to specific questions. For example, a gentleman in the 1940s observed that the burr seeds that stuck to his clothes and his dog’s fur had a tiny hook structure. Upon closer inspection, he discovered that the burrs’ gripping device was more reliable than a zipper. He eventually developed a company and produced the hook-and-loop fastener popularly known today as Velcro. Descriptive science and hypothesis-based science are in continuous dialogue.

image

  • A hypothesis is a statement/prediction that can be tested by experimentation.
  • A theory is an explanation for a set of observations or phenomena that is supported by extensive research and that can be used as the basis for further research.
  • Inductive reasoning draws on observations to infer logical conclusions based on the evidence.
  • Deductive reasoning is hypothesis-based logical reasoning that deduces conclusions from test results.
  • theory : a well-substantiated explanation of some aspect of the natural world based on knowledge that has been repeatedly confirmed through observation and experimentation
  • hypothesis : a tentative conjecture explaining an observation, phenomenon, or scientific problem that can be tested by further observation, investigation, and/or experimentation

2.4 Developing a Hypothesis

Learning objectives.

  • Distinguish between a theory and a hypothesis.
  • Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
  • Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Before describing how to develop a hypothesis it is imporant to distinguish betwee a theory and a hypothesis. A  theory  is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition. He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

Outside of science, referring to an idea as a theory often implies that it is untested—perhaps no more than a wild guess. In science, however, the term theory has no such implication. A theory is simply an explanation or interpretation of a set of phenomena. It can be untested, but it can also be extensively tested, well supported, and accepted as an accurate description of the world by the scientific community. The theory of evolution by natural selection, for example, is a theory because it is an explanation of the diversity of life on earth—not because it is untested or unsupported by scientific research. On the contrary, the evidence for this theory is overwhelmingly positive and nearly all scientists accept its basic assumptions as accurate. Similarly, the “germ theory” of disease is a theory because it is an explanation of the origin of various diseases, not because there is any doubt that many diseases are caused by microorganisms that infect the body.

A  hypothesis , on the other hand, is a specific prediction about a new phenomenon that should be observed if a particular theory is accurate. It is an explanation that relies on just a few key concepts. Hypotheses are often specific predictions about what will happen in a particular study. They are developed by considering existing evidence and using reasoning to infer what will happen in the specific context of interest. Hypotheses are often but not always derived from theories. So a hypothesis is often a prediction based on a theory but some hypotheses are a-theoretical and only after a set of observations have been made, is a theory developed. This is because theories are broad in nature and they explain larger bodies of data. So if our research question is really original then we may need to collect some data and make some observation before we can develop a broader theory.

Theories and hypotheses always have this  if-then  relationship. “ If   drive theory is correct,  then  cockroaches should run through a straight runway faster, and a branching runway more slowly, when other cockroaches are present.” Although hypotheses are usually expressed as statements, they can always be rephrased as questions. “Do cockroaches run through a straight runway faster when other cockroaches are present?” Thus deriving hypotheses from theories is an excellent way of generating interesting research questions.

But how do researchers derive hypotheses from theories? One way is to generate a research question using the techniques discussed in this chapter  and then ask whether any theory implies an answer to that question. For example, you might wonder whether expressive writing about positive experiences improves health as much as expressive writing about traumatic experiences. Although this  question  is an interesting one  on its own, you might then ask whether the habituation theory—the idea that expressive writing causes people to habituate to negative thoughts and feelings—implies an answer. In this case, it seems clear that if the habituation theory is correct, then expressive writing about positive experiences should not be effective because it would not cause people to habituate to negative thoughts and feelings. A second way to derive hypotheses from theories is to focus on some component of the theory that has not yet been directly observed. For example, a researcher could focus on the process of habituation—perhaps hypothesizing that people should show fewer signs of emotional distress with each new writing session.

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [1] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the  number  of examples they bring to mind and the other was that people base their judgments on how  easily  they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method  (although this term is much more likely to be used by philosophers of science than by scientists themselves). A researcher begins with a set of phenomena and either constructs a theory to explain or interpret them or chooses an existing theory to work with. He or she then makes a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researcher then conducts an empirical study to test the hypothesis. Finally, he or she reevaluates the theory in light of the new results and revises it if necessary. This process is usually conceptualized as a cycle because the researcher can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As  Figure 2.2  shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

Figure 4.4 Hypothetico-Deductive Method Combined With the General Model of Scientific Research in Psychology Together they form a model of theoretically motivated research.

Figure 2.2 Hypothetico-Deductive Method Combined With the General Model of Scientific Research in Psychology Together they form a model of theoretically motivated research.

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [2] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans (Zajonc & Sales, 1966) [3] in many other studies afterward).

Incorporating Theory into Your Research

When you write your research report or plan your presentation, be aware that there are two basic ways that researchers usually include theory. The first is to raise a research question, answer that question by conducting a new study, and then offer one or more theories (usually more) to explain or interpret the results. This format works well for applied research questions and for research questions that existing theories do not address. The second way is to describe one or more existing theories, derive a hypothesis from one of those theories, test the hypothesis in a new study, and finally reevaluate the theory. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.

To use theories in your research will not only give you guidance in coming up with experiment ideas and possible projects, but it lends legitimacy to your work. Psychologists have been interested in a variety of human behaviors and have developed many theories along the way. Using established theories will help you break new ground as a researcher, not limit you from developing your own ideas.

Characteristics of a Good Hypothesis

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be  logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use  deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use  inductive reasoning  which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be  positive.  That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that really it does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

Key Takeaways

  • A theory is broad in nature and explains larger bodies of data. A hypothesis is more specific and makes a prediction about the outcome of a particular study.
  • Working with theories is not “icing on the cake.” It is a basic ingredient of psychological research.
  • Like other scientists, psychologists use the hypothetico-deductive method. They construct theories to explain or interpret phenomena (or work with existing theories), derive hypotheses from their theories, test the hypotheses, and then reevaluate the theories in light of the new results.
  • Practice: Find a recent empirical research report in a professional journal. Read the introduction and highlight in different colors descriptions of theories and hypotheses.
  • Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic.  Journal of Personality and Social Psychology, 61 , 195–202. ↵
  • Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach.  Journal of Personality and Social Psychology, 13 , 83–92. ↵
  • Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵

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3.5: Developing A Hypothesis

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Theories and Hypotheses

Before describing how to develop a hypothesis, it is important to distinguish between a theory and a hypothesis. A theory is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition (1965). He pro- posed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

Outside of science, referring to an idea as a theory often implies that it is untested—perhaps no more than a wild guess. In science, however, the term theory has no such implication. A theory is simply an explanation or interpretation of a set of phenomena. It can be untested, but it can also be extensively tested, well supported, and accepted as an accurate description of the world by the scientific community. The theory of evolution by natural selection, for example, is a theory because it is an explanation of the diversity of life on earth—not because it is untested or unsupported by scientific research. On the contrary, the evidence for this theory is overwhelmingly positive and nearly all scientists accept its basic assumptions as accurate. Similarly, the “germ theory” of disease is a theory because it is an explanation of the origin of various diseases, not because there is any doubt that many diseases are caused by microorganisms that infect the body.

A hypothesis , on the other hand, is a specific prediction about a new phenomenon that should be observed if a particular theory is accurate. It is an explanation that relies on just a few key concepts. Hypotheses are often specific predictions about what will happen in a particular study. They are developed by considering existing evidence and using reasoning to infer what will happen in the specific context of interest. Hypotheses are often but not always derived from theories. So a hypothesis is often a prediction based on a theory but some hypotheses are atheoretical and only after a set of observations has been made is a theory developed. This is because theories are broad in nature and explain larger bodies of data. So if our research question is really original then we may need to collect some data and make some observations before we can develop a broader theory.

Theories and hypotheses always have this if-then relation- ship. “ If drive theory is correct, then cockroaches should run through a straight runway faster and through a branching runway more slowly when other cockroaches are present.” Although hypotheses are usually expressed as statements, they can always be rephrased as questions. “Do cockroaches run through a straight runway faster when other cockroaches are present?” Thus, deriving hypotheses from theories is an excellent way of generating interesting research questions.

But how do researchers derive hypotheses from theories? One way is to generate a research question using the techniques discussed in this chapter and then ask whether any theory implies an answer to that question. For example, you might wonder whether expressive writing about positive experiences improves health as much as expressive writing about traumatic experiences. Although this question is an interesting one on its own, you might then ask whether the habituation theory—the idea that expressive writing causes people to habituate to negative thoughts and feel- ings—implies an answer. In this case, it seems clear that if the habituation theory is correct, then expressive writing about positive experiences should not be effective because it would not cause people to habituate to negative thoughts and feelings. A second way to derive hypotheses from theories is to focus on some component of the theory that has not yet been directly observed. For example, a researcher could focus on the process of habituation—perhaps hypothesizing that people should show fewer signs of emotional distress with each new writing session.

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991). Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the number of examples they bring to mind and the other was that people base their judgments on how easily they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged them- selves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is some- times called the hypothetico-deductive method (although this term is much more likely to be used by philosophers of science than by scientists themselves). Researchers begin with a set of phenomena and either construct a theory to explain or interpret the phenomena or choose an existing theory to work with. They then make a prediction about some new phenomenon that should be observed if the theory is correct. Again, this pre- diction is called a hypothesis. The researchers then conduct an empirical study to test the hypothesis. Finally, they reevaluate the theory in light of the new results and revise it if necessary. This process is usually conceptualized as a cycle because the researchers can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As Figure \(\PageIndex{1}\) shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

Behaviorism_1.gif

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969). The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cock- roach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cock- roaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans [Zajonc & Sales, 1966] in many other studies afterward).

Incorporating Theory into Your Research

When you write your research report or plan your presentation, be aware that there are two basic ways that researchers usually include theory. The first is to raise a research question, answer that question by conducting a new study, and then offer one or more theories (usually more) to explain or interpret the results. This format works well for applied research questions and for research questions that existing theories do not address. The second way is to describe one or more existing theories, derive a hypothesis from one of those theories, test the hypothesis in a new study, and finally reevaluate the theory. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.

To use theories in your research will not only give you guidance in coming up with experiment ideas and possible projects, but it lends legitimacy to your work. Psychologists have been interested in a variety of human behaviors and have developed many theories along the way. Using established theories will help you break new ground as a researcher, not limit you from developing your own ideas.

Characteristics of a Good Hypothesis

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science, and it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be logical . As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, how- ever, when there is no theory to inform our hypothesis, we use inductive reasoning which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be positive . That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that it really does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

Deductive reasoning vs. inductive reasoning

Here's a look at the differences between deductive reasoning and inductive reasoning, with examples of each type of scientific reasoning.

  • Deductive reasoning
  • Inductive reasoning

Deductive reasoning examples

Inductive reasoning examples.

  • Abductive reasoning

Additional resources

You don't have to be Sherlock Holmes to use your powers of deductive reasoning … or would that be inductive reasoning?

So what's the difference between inductive and deductive reasoning?

During the scientific process, deductive reasoning is used to reach a logical and true conclusion. Another type of reasoning, inductive, is also commonly used. People often confuse deductive reasoning with inductive reasoning; however, important distinctions separate these two pathways to a logical conclusion.

What is deductive reasoning?

Deductive reasoning, also known as deduction, is a basic form of reasoning. It starts out with a general statement, or hypothesis, and examines the possibilities to reach a specific, logical conclusion, according to Norman Herr , a professor of secondary education at California State University in Northridge. The scientific method uses deduction to test hypotheses and theories, which predict certain outcomes if they are correct, said Dr. Sylvia Wassertheil-Smoller , a researcher and professor emerita at Albert Einstein College of Medicine. 

"We go from the general — the theory — to the specific — the observations," Wassertheil-Smoller told Live Science.

Sylvia Wassertheil-Smoller is a distinguished university professor emerita, Department of Epidemiology & Population Health (Epidemiology) at the Albert Einstein College of Medicine in New York. She's led large national studies on women's health, heart disease and stroke prevention, and has published over 300 scientific articles, as well as a book on medical research methods.

In deductive reasoning there is a first premise, then a second premise and finally an inference (a conclusion based on reasoning and evidence). A common form of deductive reasoning is the syllogism, in which two statements — a major premise and a minor premise — together reach a logical conclusion. For example, the major premise "Every A is B" could be followed by the minor premise, "This C is A." Those statements would lead to the conclusion "This C is B." Syllogisms are considered a good way to test deductive reasoning to make sure the argument is valid.

For example, "All spiders have eight legs. A tarantula is a spider. Therefore, tarantulas have eight legs." For deductive reasoning to be sound, the hypothesis must be correct. It is assumed that the statements, "All spiders have eight legs" and "a tarantula is a spider" are true. Therefore, the conclusion is logical and true. In deductive reasoning, if something is true of a class of things in general, it is also true for all members of that class. 

Deductive conclusions are reliable provided the premises are true, according to Herr. The argument, "All bald men are grandfathers. Harold is bald. Therefore, Harold is a grandfather," is valid logically, but it is untrue because the original premise is false.

What is inductive reasoning

While deductive reasoning begins with a premise that is proven through observations, inductive reasoning extracts a likely (but not certain) premise from specific and limited observations. There is data, and then conclusions are drawn from the data; this is called inductive logic, according to  the University of Illinois in Springfield.

"In inductive inference, we go from the specific to the general. We make many observations, discern a pattern, make a generalization, and infer an explanation or a theory," Wassertheil-Smoller told Live Science. "In science, there is a constant interplay between inductive inference (based on observations) and deductive inference (based on theory), until we get closer and closer to the 'truth,' which we can only approach but not ascertain with complete certainty." 

In other words, the reliability of a conclusion made with inductive logic depends on the completeness of the observations. For instance, let's say that you have a bag of coins; you pull three coins from the bag, and each coin is a penny. Using inductive logic, you might then propose that all of the coins in the bag are pennies."Even though all of the initial observations — that each coin taken from the bag was a penny — are correct, inductive reasoning does not guarantee that the conclusion will be true. 

Here's another example: "Penguins are birds. Penguins can't fly. Therefore, all birds can't fly." The conclusion does not follow logically from the statements.

Nevertheless, inductive reasoning has its place in the scientific method , and scientists use it to form hypotheses and theories . Deductive reasoning then allows them to apply the theories to specific situations.

Here are some examples of deductive reasoning:

Major premise:  All mammals have backbones. Minor premise:  Humans are mammals. Conclusion:  Humans have backbones.

Major premise:  All birds lay eggs. Minor premise:  Pigeons are birds. Conclusion:  Pigeons lay eggs.

Major premise:  All plants perform photosynthesis. Minor premise:  A cactus is a plant. Conclusion:  A cactus performs photosynthesis.

Here are some examples of inductive reasoning:

Data:  I see fireflies in my backyard every summer. Hypothesis:  This summer, I will probably see fireflies in my backyard.

Data:  I tend to catch colds when people around me are sick. Hypothesis:  Colds are infectious.

Data:  Every dog I meet is friendly. 

Hypothesis:  Most dogs are usually friendly.

What is abductive reasoning

Another form of scientific reasoning that diverges from inductive and deductive reasoning is abductive. Abductive reasoning usually starts with an obviously incomplete set of observations and proceeds to the likeliest possible explanation for the data, a ccording to Butte College in Oroville, California. It is based on making and testing hypotheses using the best information available. It often entails making an educated guess after observing a phenomenon for which there is no clear explanation. 

For example, a person walks into their living room and finds torn-up papers all over the floor. The person's dog has been alone in the apartment all day. The person concludes that the dog tore up the papers because it is the most likely scenario. It's possible that a family member with a key to the apartment destroyed the papers, or it may have been done by the landlord, but the dog theory is the most likely conclusion.

Abductive reasoning is useful for forming hypotheses to be tested. Abductive reasoning is often used by doctors who make a diagnosis based on test results, and by jurors who make decisions based on the evidence presented to them.

  • This guide from Scholastic provides ideas for teaching younger kids all about scientific reasoning. PBS has put together some video clips and games about deductive and inductive reasoning.This book written by Christopher Moore provides information on how to use scientific reasoning in the classroom. 

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The potential of working hypotheses for deductive exploratory research

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  • Published: 08 December 2020
  • Volume 55 , pages 1703–1725, ( 2021 )

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  • Mattia Casula   ORCID: orcid.org/0000-0002-7081-8153 1 ,
  • Nandhini Rangarajan 2 &
  • Patricia Shields   ORCID: orcid.org/0000-0002-0960-4869 2  

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While hypotheses frame explanatory studies and provide guidance for measurement and statistical tests, deductive, exploratory research does not have a framing device like the hypothesis. To this purpose, this article examines the landscape of deductive, exploratory research and offers the working hypothesis as a flexible, useful framework that can guide and bring coherence across the steps in the research process. The working hypothesis conceptual framework is introduced, placed in a philosophical context, defined, and applied to public administration and comparative public policy. 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 when exploratory research is deductive.

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1 Introduction

Exploratory research is generally considered to be inductive and qualitative (Stebbins 2001 ). Exploratory qualitative studies adopting an inductive approach do not lend themselves to a priori theorizing and building upon prior bodies of knowledge (Reiter 2013 ; Bryman 2004 as cited in Pearse 2019 ). Juxtaposed against quantitative studies that employ deductive confirmatory approaches, exploratory qualitative research is often criticized for lack of methodological rigor and tentativeness in results (Thomas and Magilvy 2011 ). This paper focuses on the neglected topic of deductive, exploratory research and proposes working hypotheses as a useful framework for these studies.

To emphasize that certain types of applied research lend themselves more easily to deductive approaches, to address the downsides of exploratory qualitative research, and to ensure qualitative rigor in exploratory research, a significant body of work on deductive qualitative approaches has emerged (see for example, Gilgun 2005 , 2015 ; Hyde 2000 ; Pearse 2019 ). According to Gilgun ( 2015 , p. 3) the use of conceptual frameworks derived from comprehensive reviews of literature and a priori theorizing were common practices in qualitative research prior to the publication of Glaser and Strauss’s ( 1967 ) The Discovery of Grounded Theory . Gilgun ( 2015 ) coined the terms Deductive Qualitative Analysis (DQA) to arrive at some sort of “middle-ground” such that the benefits of a priori theorizing (structure) and allowing room for new theory to emerge (flexibility) are reaped simultaneously. According to Gilgun ( 2015 , p. 14) “in DQA, the initial conceptual framework and hypotheses are preliminary. The purpose of DQA is to come up with a better theory than researchers had constructed at the outset (Gilgun 2005 , 2009 ). Indeed, the production of new, more useful hypotheses is the goal of DQA”.

DQA provides greater level of structure for both the experienced and novice qualitative researcher (see for example Pearse 2019 ; Gilgun 2005 ). According to Gilgun ( 2015 , p. 4) “conceptual frameworks are the sources of hypotheses and sensitizing concepts”. Sensitizing concepts frame the exploratory research process and guide the researcher’s data collection and reporting efforts. Pearse ( 2019 ) discusses the usefulness for deductive thematic analysis and pattern matching to help guide DQA in business research. Gilgun ( 2005 ) discusses the usefulness of DQA for family research.

Given these rationales for DQA in exploratory research, the overarching purpose of this paper is to contribute to that growing corpus of work on deductive qualitative research. This paper is specifically aimed at guiding novice researchers and student scholars to the working hypothesis as a useful a priori framing tool. The applicability of the working hypothesis as a tool that provides more structure during the design and implementation phases of exploratory research is discussed in detail. Examples of research projects in public administration that use the working hypothesis as a framing tool for deductive exploratory research are provided.

In the next section, we introduce the three types of research purposes. Second, we examine the nature of the exploratory research purpose. Third, we provide a definition of working hypothesis. Fourth, we explore the philosophical roots of methodology to see where exploratory research fits. Fifth, we connect the discussion to the dominant research approaches (quantitative, qualitative and mixed methods) to see where deductive exploratory research fits. Sixth, we examine the nature of theory and the role of the hypothesis in theory. We contrast formal hypotheses and working hypotheses. Seven, we provide examples of student and scholarly work that illustrates how working hypotheses are developed and operationalized. Lastly, this paper synthesizes previous discussion with concluding remarks.

2 Three types of research purposes

The literature identifies three basic types of research purposes—explanation, description and exploration (Babbie 2007 ; Adler and Clark 2008 ; Strydom 2013 ; Shields and Whetsell 2017 ). Research purposes are similar to research questions; however, they focus on project goals or aims instead of questions.

Explanatory research answers the “why” question (Babbie 2007 , pp. 89–90), by explaining “why things are the way they are”, and by looking “for causes and reasons” (Adler and Clark 2008 , p. 14). Explanatory research is closely tied to hypothesis testing. Theory is tested using deductive reasoning, which goes from the general to the specific (Hyde 2000 , p. 83). Hypotheses provide a frame for explanatory research connecting the research purpose to other parts of the research process (variable construction, choice of data, statistical tests). They help provide alignment or coherence across stages in the research process and provide ways to critique the strengths and weakness of the study. For example, were the hypotheses grounded in the appropriate arguments and evidence in the literature? Are the concepts imbedded in the hypotheses appropriately measured? Was the best statistical test used? When the analysis is complete (hypothesis is tested), the results generally answer the research question (the evidence supported or failed to support the hypothesis) (Shields and Rangarajan 2013 ).

Descriptive research addresses the “What” question and is not primarily concerned with causes (Strydom 2013 ; Shields and Tajalli 2006 ). It lies at the “midpoint of the knowledge continuum” (Grinnell 2001 , p. 248) between exploration and explanation. Descriptive research is used in both quantitative and qualitative research. A field researcher might want to “have a more highly developed idea of social phenomena” (Strydom 2013 , p. 154) and develop thick descriptions using inductive logic. In science, categorization and classification systems such as the periodic table of chemistry or the taxonomies of biology inform descriptive research. These baseline classification systems are a type of theorizing and allow researchers to answer questions like “what kind” of plants and animals inhabit a forest. The answer to this question would usually be displayed in graphs and frequency distributions. This is also the data presentation system used in the social sciences (Ritchie and Lewis 2003 ; Strydom 2013 ). For example, if a scholar asked, what are the needs of homeless people? A quantitative approach would include a survey that incorporated a “needs” classification system (preferably based on a literature review). The data would be displayed as frequency distributions or as charts. Description can also be guided by inductive reasoning, which draws “inferences from specific observable phenomena to general rules or knowledge expansion” (Worster 2013 , p. 448). Theory and hypotheses are generated using inductive reasoning, which begins with data and the intention of making sense of it by theorizing. Inductive descriptive approaches would use a qualitative, naturalistic design (open ended interview questions with the homeless population). The data could provide a thick description of the homeless context. For deductive descriptive research, categories, serve a purpose similar to hypotheses for explanatory research. If developed with thought and a connection to the literature, categories can serve as a framework that inform measurement, link to data collection mechanisms and to data analysis. Like hypotheses they can provide horizontal coherence across the steps in the research process.

Table  1 demonstrated these connections for deductive, descriptive and explanatory research. The arrow at the top emphasizes the horizontal or across the research process view we emphasize. This article makes the case that the working hypothesis can serve the same purpose as the hypothesis for deductive, explanatory research and categories for deductive descriptive research. The cells for exploratory research are filled in with question marks.

The remainder of this paper focuses on exploratory research and the answers to questions found in the table:

What is the philosophical underpinning of exploratory, deductive research?

What is the Micro-conceptual framework for deductive exploratory research? [ As is clear from the article title we introduce the working hypothesis as the answer .]

How does the working hypothesis inform the methodologies and evidence collection of deductive exploratory research?

How does the working hypothesis inform data analysis of deductive exploratory research?

3 The nature of exploratory research purpose

Explorers enter the unknown to discover something new. The process can be fraught with struggle and surprises. Effective explorers creatively resolve unexpected problems. While we typically think of explorers as pioneers or mountain climbers, exploration is very much linked to the experience and intention of the explorer. Babies explore as they take their first steps. The exploratory purpose resonates with these insights. Exploratory research, like reconnaissance, is a type of inquiry that is in the preliminary or early stages (Babbie 2007 ). It is associated with discovery, creativity and serendipity (Stebbins 2001 ). But the person doing the discovery, also defines the activity or claims the act of exploration. It “typically occurs when a researcher examines a new interest or when the subject of study itself is relatively new” (Babbie 2007 , p. 88). Hence, exploration has an open character that emphasizes “flexibility, pragmatism, and the particular, biographically specific interests of an investigator” (Maanen et al. 2001 , p. v). These three purposes form a type of hierarchy. An area of inquiry is initially explored . This early work lays the ground for, description which in turn becomes the basis for explanation . Quantitative, explanatory studies dominate contemporary high impact journals (Twining et al. 2017 ).

Stebbins ( 2001 ) makes the point that exploration is often seen as something like a poor stepsister to confirmatory or hypothesis testing research. He has a problem with this because we live in a changing world and what is settled today will very likely be unsettled in the near future and in need of exploration. Further, exploratory research “generates initial insights into the nature of an issue and develops questions to be investigated by more extensive studies” (Marlow 2005 , p. 334). Exploration is widely applicable because all research topics were once “new.” Further, all research topics have the possibility of “innovation” or ongoing “newness”. Exploratory research may be appropriate to establish whether a phenomenon exists (Strydom 2013 ). The point here, of course, is that the exploratory purpose is far from trivial.

Stebbins’ Exploratory Research in the Social Sciences ( 2001 ), is the only book devoted to the nature of exploratory research as a form of social science inquiry. He views it as a “broad-ranging, purposive, systematic prearranged undertaking designed to maximize the discovery of generalizations leading to description and understanding of an area of social or psychological life” (p. 3). It is science conducted in a way distinct from confirmation. According to Stebbins ( 2001 , p. 6) the goal is discovery of potential generalizations, which can become future hypotheses and eventually theories that emerge from the data. He focuses on inductive logic (which stimulates creativity) and qualitative methods. He does not want exploratory research limited to the restrictive formulas and models he finds in confirmatory research. He links exploratory research to Glaser and Strauss’s ( 1967 ) flexible, immersive, Grounded Theory. Strydom’s ( 2013 ) analysis of contemporary social work research methods books echoes Stebbins’ ( 2001 ) position. Stebbins’s book is an important contribution, but it limits the potential scope of this flexible and versatile research purpose. If we accepted his conclusion, we would delete the “Exploratory” row from Table  1 .

Note that explanatory research can yield new questions, which lead to exploration. Inquiry is a process where inductive and deductive activities can occur simultaneously or in a back and forth manner, particularly as the literature is reviewed and the research design emerges. Footnote 1 Strict typologies such as explanation, description and exploration or inductive/deductive can obscures these larger connections and processes. We draw insight from Dewey’s ( 1896 ) vision of inquiry as depicted in his seminal “Reflex Arc” article. He notes that “stimulus” and “response” like other dualities (inductive/deductive) exist within a larger unifying system. Yet the terms have value. “We need not abandon terms like stimulus and response, so long as we remember that they are attached to events based upon their function in a wider dynamic context, one that includes interests and aims” (Hildebrand 2008 , p. 16). So too, in methodology typologies such as deductive/inductive capture useful distinctions with practical value and are widely used in the methodology literature.

We argue that there is a role for exploratory, deductive, and confirmatory research. We maintain all types of research logics and methods should be in the toolbox of exploratory research. First, as stated above, it makes no sense on its face to identify an extremely flexible purpose that is idiosyncratic to the researcher and then basically restrict its use to qualitative, inductive, non-confirmatory methods. Second, Stebbins’s ( 2001 ) work focused on social science ignoring the policy sciences. Exploratory research can be ideal for immediate practical problems faced by policy makers, who could find a framework of some kind useful. Third, deductive, exploratory research is more intentionally connected to previous research. Some kind of initial framing device is located or designed using the literature. This may be very important for new scholars who are developing research skills and exploring their field and profession. Stebbins’s insights are most pertinent for experienced scholars. Fourth, frameworks and deductive logic are useful for comparative work because some degree of consistency across cases is built into the design.

As we have seen, the hypotheses of explanatory and categories of descriptive research are the dominate frames of social science and policy science. We certainly concur that neither of these frames makes a lot of sense for exploratory research. They would tend to tie it down. We see the problem as a missing framework or missing way to frame deductive, exploratory research in the methodology literature. Inductive exploratory research would not work for many case studies that are trying to use evidence to make an argument. What exploratory deductive case studies need is a framework that incorporates flexibility. This is even more true for comparative case studies. A framework of this sort could be usefully applied to policy research (Casula 2020a ), particularly evaluative policy research, and applied research generally. We propose the Working Hypothesis as a flexible conceptual framework and as a useful tool for doing exploratory studies. It can be used as an evaluative criterion particularly for process evaluation and is useful for student research because students can develop theorizing skills using the literature.

Table  1 included a column specifying the philosophical basis for each research purpose. Shifting gears to the philosophical underpinning of methodology provides useful additional context for examination of deductive, exploratory research.

4 What is a working hypothesis

The working hypothesis is first and foremost a hypothesis or a statement of expectation that is tested in action. The term “working” suggest that these hypotheses are subject to change, are provisional and the possibility of finding contradictory evidence is real. In addition, a “working” hypothesis is active, it is a tool in an ongoing process of inquiry. If one begins with a research question, the working hypothesis could be viewed as a statement or group of statements that answer the question. It “works” to move purposeful inquiry forward. “Working” also implies some sort of community, mostly we work together in relationship to achieve some goal.

Working Hypothesis is a term found in earlier literature. Indeed, both pioneering pragmatists, John Dewey and George Herbert Mead use the term working hypothesis in important nineteenth century works. For both Dewey and Mead, the notion of a working hypothesis has a self-evident quality and it is applied in a big picture context. Footnote 2

Most notably, Dewey ( 1896 ), in one of his most pivotal early works (“Reflex Arc”), used “working hypothesis” to describe a key concept in psychology. “The idea of the reflex arc has upon the whole come nearer to meeting this demand for a general working hypothesis than any other single concept (Italics added)” (p. 357). The notion of a working hypothesis was developed more fully 42 years later, in Logic the Theory of Inquiry , where Dewey developed the notion of a working hypothesis that operated on a smaller scale. He defines working hypotheses as a “provisional, working means of advancing investigation” (Dewey 1938 , pp. 142). Dewey’s definition suggests that working hypotheses would be useful toward the beginning of a research project (e.g., exploratory research).

Mead ( 1899 ) used working hypothesis in a title of an American Journal of Sociology article “The Working Hypothesis and Social Reform” (italics added). He notes that a scientist’s foresight goes beyond testing a hypothesis.

Given its success, he may restate his world from this standpoint and get the basis for further investigation that again always takes the form of a problem. The solution of this problem is found over again in the possibility of fitting his hypothetical proposition into the whole within which it arises. And he must recognize that this statement is only a working hypothesis at the best, i.e., he knows that further investigation will show that the former statement of his world is only provisionally true, and must be false from the standpoint of a larger knowledge, as every partial truth is necessarily false over against the fuller knowledge which he will gain later (Mead 1899 , p. 370).

Cronbach ( 1975 ) developed a notion of working hypothesis consistent with inductive reasoning, but for him, the working hypothesis is a product or result of naturalistic inquiry. He makes the case that naturalistic inquiry is highly context dependent and therefore results or seeming generalizations that may come from a study and should be viewed as “working hypotheses”, which “are tentative both for the situation in which they first uncovered and for other situations” (as cited in Gobo 2008 , p. 196).

A quick Google scholar search using the term “working hypothesis” show that it is widely used in twentieth and twenty-first century science, particularly in titles. In these articles, the working hypothesis is treated as a conceptual tool that furthers investigation in its early or transitioning phases. We could find no explicit links to exploratory research. The exploratory nature of the problem is expressed implicitly. Terms such as “speculative” (Habib 2000 , p. 2391) or “rapidly evolving field” (Prater et al. 2007 , p. 1141) capture the exploratory nature of the study. The authors might describe how a topic is “new” or reference “change”. “As a working hypothesis, the picture is only new, however, in its interpretation” (Milnes 1974 , p. 1731). In a study of soil genesis, Arnold ( 1965 , p. 718) notes “Sequential models, formulated as working hypotheses, are subject to further investigation and change”. Any 2020 article dealing with COVID-19 and respiratory distress would be preliminary almost by definition (Ciceri et al. 2020 ).

5 Philosophical roots of methodology

According to Kaplan ( 1964 , p. 23) “the aim of methodology is to help us understand, in the broadest sense not the products of scientific inquiry but the process itself”. Methods contain philosophical principles that distinguish them from other “human enterprises and interests” (Kaplan 1964 , p. 23). Contemporary research methodology is generally classified as quantitative, qualitative and mixed methods. Leading scholars of methodology have associated each with a philosophical underpinning—positivism (or post-positivism), interpretivism or constructivist and pragmatism, respectively (Guba 1987 ; Guba and Lincoln 1981 ; Schrag 1992 ; Stebbins 2001 ; Mackenzi and Knipe 2006 ; Atieno 2009 ; Levers 2013 ; Morgan 2007 ; O’Connor et al. 2008 ; Johnson and Onwuegbuzie 2004 ; Twining et al. 2017 ). This section summarizes how the literature often describes these philosophies and informs contemporary methodology and its literature.

Positivism and its more contemporary version, post-positivism, maintains an objectivist ontology or assumes an objective reality, which can be uncovered (Levers 2013 ; Twining et al. 2017 ). Footnote 3 Time and context free generalizations are possible and “real causes of social scientific outcomes can be determined reliably and validly (Johnson and Onwuegbunzie 2004 , p. 14). Further, “explanation of the social world is possible through a logical reduction of social phenomena to physical terms”. It uses an empiricist epistemology which “implies testability against observation, experimentation, or comparison” (Whetsell and Shields 2015 , pp. 420–421). Correspondence theory, a tenet of positivism, asserts that “to each concept there corresponds a set of operations involved in its scientific use” (Kaplan 1964 , p. 40).

The interpretivist, constructivists or post-modernist approach is a reaction to positivism. It uses a relativist ontology and a subjectivist epistemology (Levers 2013 ). In this world of multiple realities, context free generalities are impossible as is the separation of facts and values. Causality, explanation, prediction, experimentation depend on assumptions about the correspondence between concepts and reality, which in the absence of an objective reality is impossible. Empirical research can yield “contextualized emergent understanding rather than the creation of testable theoretical structures” (O’Connor et al. 2008 , p. 30). The distinctively different world views of positivist/post positivist and interpretivist philosophy is at the core of many controversies in methodology, social and policy science literature (Casula 2020b ).

With its focus on dissolving dualisms, pragmatism steps outside the objective/subjective debate. Instead, it asks, “what difference would it make to us if the statement were true” (Kaplan 1964 , p. 42). Its epistemology is connected to purposeful inquiry. Pragmatism has a “transformative, experimental notion of inquiry” anchored in pluralism and a focus on constructing conceptual and practical tools to resolve “problematic situations” (Shields 1998 ; Shields and Rangarajan 2013 ). Exploration and working hypotheses are most comfortably situated within the pragmatic philosophical perspective.

6 Research approaches

Empirical investigation relies on three types of methodology—quantitative, qualitative and mixed methods.

6.1 Quantitative methods

Quantitative methods uses deductive logic and formal hypotheses or models to explain, predict, and eventually establish causation (Hyde 2000 ; Kaplan 1964 ; Johnson and Onwuegbunzie 2004 ; Morgan 2007 ). Footnote 4 The correspondence between the conceptual and empirical world make measures possible. Measurement assigns numbers to objects, events or situations and allows for standardization and subtle discrimination. It also allows researchers to draw on the power of mathematics and statistics (Kaplan 1964 , pp. 172–174). Using the power of inferential statistics, quantitative research employs research designs, which eliminate competing hypotheses. It is high in external validity or the ability to generalize to the whole. The research results are relatively independent of the researcher (Johnson & Onwuegbunzie 2004 ).

Quantitative methods depend on the quality of measurement and a priori conceptualization, and adherence to the underlying assumptions of inferential statistics. Critics charge that hypotheses and frameworks needlessly constrain inquiry (Johnson and Onwuegbunzie 2004 , p. 19). Hypothesis testing quantitative methods support the explanatory purpose.

6.2 Qualitative methods

Qualitative researchers who embrace the post-modern, interpretivist view, Footnote 5 question everything about the nature of quantitative methods (Willis et al. 2007 ). Rejecting the possibility of objectivity, correspondence between ideas and measures, and the constraints of a priori theorizing they focus on “unique impressions and understandings of events rather than to generalize the findings” (Kolb 2012 , p. 85). Characteristics of traditional qualitative research include “induction, discovery, exploration, theory/hypothesis generation and the researcher as the primary ‘instrument’ of data collection” (Johnson and Onwuegbunzie 2004 , p. 18). It also concerns itself with forming “unique impressions and understandings of events rather than to generalize findings” (Kolb 2012 , p. 85). The data of qualitative methods are generated via interviews, direct observation, focus groups and analysis of written records or artifacts.

Qualitative methods provide for understanding and “description of people’s personal experiences of phenomena”. They enable descriptions of detailed “phenomena as they are situated and embedded in local contexts.” Researchers use naturalistic settings to “study dynamic processes” and explore how participants interpret experiences. Qualitative methods have an inherent flexibility, allowing researchers to respond to changes in the research setting. They are particularly good at narrowing to the particular and on the flipside have limited external validity (Johnson and Onwuegbunzie 2004 , p. 20). Instead of specifying a suitable sample size to draw conclusions, qualitative research uses the notion of saturation (Morse 1995 ).

Saturation is used in grounded theory—a widely used and respected form of qualitative research, and a well-known interpretivist qualitative research method. Introduced by Glaser and Strauss ( 1967 ), this “grounded on observation” (Patten and Newhart 2000 , p. 27) methodology, focuses on “the creation of emergent understanding” (O’Connor et al. 2008 , p. 30). It uses the Constant Comparative method, whereby researchers develop theory from data as they code and analyze at the same time. Data collection, coding and analysis along with theoretical sampling are systematically combined to generate theory (Kolb 2012 , p. 83). The qualitative methods discussed here support exploratory research.

A close look at the two philosophies and assumptions of quantitative and qualitative research suggests two contradictory world views. The literature has labeled these contradictory views the Incompatibility Theory, which sets up a quantitative versus qualitative tension similar to the seeming separation of art and science or fact and values (Smith 1983a , b ; Guba 1987 ; Smith and Heshusius 1986 ; Howe 1988 ). The incompatibility theory does not make sense in practice. Yin ( 1981 , 1992 , 2011 , 2017 ), a prominent case study scholar, showcases a deductive research methodology that crosses boundaries using both quantaitive and qualitative evidence when appropriate.

6.3 Mixed methods

Turning the “Incompatibility Theory” on its head, Mixed Methods research “combines elements of qualitative and quantitative research approaches … for the broad purposes of breadth and depth of understanding and corroboration” (Johnson et al. 2007 , p. 123). It does this by partnering with philosophical pragmatism. Footnote 6 Pragmatism is productive because “it offers an immediate and useful middle position philosophically and methodologically; it offers a practical and outcome-oriented method of inquiry that is based on action and leads, iteratively, to further action and the elimination of doubt; it offers a method for selecting methodological mixes that can help researchers better answer many of their research questions” (Johnson and Onwuegbunzie 2004 , p. 17). What is theory for the pragmatist “any theoretical model is for the pragmatist, nothing more than a framework through which problems are perceived and subsequently organized ” (Hothersall 2019 , p. 5).

Brendel ( 2009 ) constructed a simple framework to capture the core elements of pragmatism. Brendel’s four “p”’s—practical, pluralism, participatory and provisional help to show the relevance of pragmatism to mixed methods. Pragmatism is purposeful and concerned with the practical consequences. The pluralism of pragmatism overcomes quantitative/qualitative dualism. Instead, it allows for multiple perspectives (including positivism and interpretivism) and, thus, gets around the incompatibility problem. Inquiry should be participatory or inclusive of the many views of participants, hence, it is consistent with multiple realities and is also tied to the common concern of a problematic situation. Finally, all inquiry is provisional . This is compatible with experimental methods, hypothesis testing and consistent with the back and forth of inductive and deductive reasoning. Mixed methods support exploratory research.

Advocates of mixed methods research note that it overcomes the weaknesses and employs the strengths of quantitative and qualitative methods. Quantitative methods provide precision. The pictures and narrative of qualitative techniques add meaning to the numbers. Quantitative analysis can provide a big picture, establish relationships and its results have great generalizability. On the other hand, the “why” behind the explanation is often missing and can be filled in through in-depth interviews. A deeper and more satisfying explanation is possible. Mixed-methods brings the benefits of triangulation or multiple sources of evidence that converge to support a conclusion. It can entertain a “broader and more complete range of research questions” (Johnson and Onwuegbunzie 2004 , p. 21) and can move between inductive and deductive methods. Case studies use multiple forms of evidence and are a natural context for mixed methods.

One thing that seems to be missing from mixed method literature and explicit design is a place for conceptual frameworks. For example, Heyvaert et al. ( 2013 ) examined nine mixed methods studies and found an explicit framework in only two studies (transformative and pragmatic) (p. 663).

7 Theory and hypotheses: where is and what is theory?

Theory is key to deductive research. In essence, empirical deductive methods test theory. Hence, we shift our attention to theory and the role and functions of the hypotheses in theory. Oppenheim and Putnam ( 1958 ) note that “by a ‘theory’ (in the widest sense) we mean any hypothesis, generalization or law (whether deterministic or statistical) or any conjunction of these” (p. 25). Van Evera ( 1997 ) uses a similar and more complex definition “theories are general statements that describe and explain the causes of effects of classes of phenomena. They are composed of causal laws or hypotheses, explanations, and antecedent conditions” (p. 8). Sutton and Staw ( 1995 , p. 376) in a highly cited article “What Theory is Not” assert the that hypotheses should contain logical arguments for “why” the hypothesis is expected. Hypotheses need an underlying causal argument before they can be considered theory. The point of this discussion is not to define theory but to establish the importance of hypotheses in theory.

Explanatory research is implicitly relational (A explains B). The hypotheses of explanatory research lay bare these relationships. Popular definitions of hypotheses capture this relational component. For example, the Cambridge Dictionary defines a hypothesis a “an idea or explanation for something that is based on known facts but has not yet been proven”. Vocabulary.Com’s definition emphasizes explanation, a hypothesis is “an idea or explanation that you then test through study and experimentation”. According to Wikipedia a hypothesis is “a proposed explanation for a phenomenon”. Other definitions remove the relational or explanatory reference. The Oxford English Dictionary defines a hypothesis as a “supposition or conjecture put forth to account for known facts.” Science Buddies defines a hypothesis as a “tentative, testable answer to a scientific question”. According to the Longman Dictionary the hypothesis is “an idea that can be tested to see if it is true or not”. The Urban Dictionary states a hypothesis is “a prediction or educated-guess based on current evidence that is yet be tested”. We argue that the hypotheses of exploratory research— working hypothesis — are not bound by relational expectations. It is this flexibility that distinguishes the working hypothesis.

Sutton and Staw (1995) maintain that hypotheses “serve as crucial bridges between theory and data, making explicit how the variables and relationships that follow from a logical argument will be operationalized” (p. 376, italics added). The highly rated journal, Computers and Education , Twining et al. ( 2017 ) created guidelines for qualitative research as a way to improve soundness and rigor. They identified the lack of alignment between theoretical stance and methodology as a common problem in qualitative research. In addition, they identified a lack of alignment between methodology, design, instruments of data collection and analysis. The authors created a guidance summary, which emphasized the need to enhance coherence throughout elements of research design (Twining et al. 2017 p. 12). Perhaps the bridging function of the hypothesis mentioned by Sutton and Staw (1995) is obscured and often missing in qualitative methods. Working hypotheses can be a tool to overcome this problem.

For reasons, similar to those used by mixed methods scholars, we look to classical pragmatism and the ideas of John Dewey to inform our discussion of theory and working hypotheses. Dewey ( 1938 ) treats theory as a tool of empirical inquiry and uses a map metaphor (p. 136). Theory is like a map that helps a traveler navigate the terrain—and should be judged by its usefulness. “There is no expectation that a map is a true representation of reality. Rather, it is a representation that allows a traveler to reach a destination (achieve a purpose). Hence, theories should be judged by how well they help resolve the problem or achieve a purpose ” (Shields and Rangarajan 2013 , p. 23). Note that we explicitly link theory to the research purpose. Theory is never treated as an unimpeachable Truth, rather it is a helpful tool that organizes inquiry connecting data and problem. Dewey’s approach also expands the definition of theory to include abstractions (categories) outside of causation and explanation. The micro-conceptual frameworks Footnote 7 introduced in Table  1 are a type of theory. We define conceptual frameworks as the “way the ideas are organized to achieve the project’s purpose” (Shields and Rangarajan 2013 p. 24). Micro-conceptual frameworks do this at the very close to the data level of analysis. Micro-conceptual frameworks can direct operationalization and ways to assess measurement or evidence at the individual research study level. Again, the research purpose plays a pivotal role in the functioning of theory (Shields and Tajalli 2006 ).

8 Working hypothesis: methods and data analysis

We move on to answer the remaining questions in the Table  1 . We have established that exploratory research is extremely flexible and idiosyncratic. Given this, we will proceed with a few examples and draw out lessons for developing an exploratory purpose, building a framework and from there identifying data collection techniques and the logics of hypotheses testing and analysis. Early on we noted the value of the Working Hypothesis framework for student empirical research and applied research. The next section uses a masters level student’s work to illustrate the usefulness of working hypotheses as a way to incorporate the literature and structure inquiry. This graduate student was also a mature professional with a research question that emerged from his job and is thus an example of applied research.

Master of Public Administration student, Swift ( 2010 ) worked for a public agency and was responsible for that agency’s sexual harassment training. The agency needed to evaluate its training but had never done so before. He also had never attempted a significant empirical research project. Both of these conditions suggest exploration as a possible approach. He was interested in evaluating the training program and hence the project had a normative sense. Given his job, he already knew a lot about the problem of sexual harassment and sexual harassment training. What he did not know much about was doing empirical research, reviewing the literature or building a framework to evaluate the training (working hypotheses). He wanted a framework that was flexible and comprehensive. In his research, he discovered Lundvall’s ( 2006 ) knowledge taxonomy summarized with four simple ways of knowing ( Know - what, Know - how, Know - why, Know - who ). He asked whether his agency’s training provided the participants with these kinds of knowledge? Lundvall’s categories of knowing became the basis of his working hypotheses. Lundvall’s knowledge taxonomy is well suited for working hypotheses because it is so simple and is easy to understand intuitively. It can also be tailored to the unique problematic situation of the researcher. Swift ( 2010 , pp. 38–39) developed four basic working hypotheses:

WH1: Capital Metro provides adequate know - what knowledge in its sexual harassment training

WH2: Capital Metro provides adequate know - how knowledge in its sexual harassment training

WH3: Capital Metro provides adequate know - why knowledge in its sexual harassment training

WH4: Capital Metro provides adequate know - who knowledge in its sexual harassment training

From here he needed to determine what would determine the different kinds of knowledge. For example, what constitutes “know what” knowledge for sexual harassment training. This is where his knowledge and experience working in the field as well as the literature come into play. According to Lundvall et al. ( 1988 , p. 12) “know what” knowledge is about facts and raw information. Swift ( 2010 ) learned through the literature that laws and rules were the basis for the mandated sexual harassment training. He read about specific anti-discrimination laws and the subsequent rules and regulations derived from the laws. These laws and rules used specific definitions and were enacted within a historical context. Laws, rules, definitions and history became the “facts” of Know-What knowledge for his working hypothesis. To make this clear, he created sub-hypotheses that explicitly took these into account. See how Swift ( 2010 , p. 38) constructed the sub-hypotheses below. Each sub-hypothesis was defended using material from the literature (Swift 2010 , pp. 22–26). The sub-hypotheses can also be easily tied to evidence. For example, he could document that the training covered anti-discrimination laws.

WH1: Capital Metro provides adequate know - what knowledge in its sexual Harassment training

WH1a: The sexual harassment training includes information on anti-discrimination laws (Title VII).

WH1b: The sexual harassment training includes information on key definitions.

WH1c: The sexual harassment training includes information on Capital Metro’s Equal Employment Opportunity and Harassment policy.

WH1d: Capital Metro provides training on sexual harassment history.

Know-How knowledge refers to the ability to do something and involves skills (Lundvall and Johnson 1994 , p. 12). It is a kind of expertise in action. The literature and his experience allowed James Smith to identify skills such as how to file a claim or how to document incidents of sexual harassment as important “know-how” knowledge that should be included in sexual harassment training. Again, these were depicted as sub-hypotheses.

WH2: Capital Metro provides adequate know - how knowledge in its sexual Harassment training

WH2a: Training is provided on how to file and report a claim of harassment

WH2b: Training is provided on how to document sexual harassment situations.

WH2c: Training is provided on how to investigate sexual harassment complaints.

WH2d: Training is provided on how to follow additional harassment policy procedures protocol

Note that the working hypotheses do not specify a relationship but rather are simple declarative sentences. If “know-how” knowledge was found in the sexual harassment training, he would be able to find evidence that participants learned about how to file a claim (WH2a). The working hypothesis provides the bridge between theory and data that Sutton and Staw (1995) found missing in exploratory work. The sub-hypotheses are designed to be refined enough that the researchers would know what to look for and tailor their hunt for evidence. Figure  1 captures the generic sub-hypothesis design.

figure 1

A Common structure used in the development of working hypotheses

When expected evidence is linked to the sub-hypotheses, data, framework and research purpose are aligned. This can be laid out in a planning document that operationalizes the data collection in something akin to an architect’s blueprint. This is where the scholar explicitly develops the alignment between purpose, framework and method (Shields and Rangarajan 2013 ; Shields et al. 2019b ).

Table  2 operationalizes Swift’s working hypotheses (and sub-hypotheses). The table provide clues as to what kind of evidence is needed to determine whether the hypotheses are supported. In this case, Smith used interviews with participants and trainers as well as a review of program documents. Column one repeats the sub-hypothesis, column two specifies the data collection method (here interviews with participants/managers and review of program documents) and column three specifies the unique questions that focus the investigation. For example, the interview questions are provided. In the less precise world of qualitative data, evidence supporting a hypothesis could have varying degrees of strength. This too can be specified.

For Swift’s example, neither the statistics of explanatory research nor the open-ended questions of interpretivist, inductive exploratory research is used. The deductive logic of inquiry here is somewhat intuitive and similar to a detective (Ulriksen and Dadalauri 2016 ). It is also a logic used in international law (Worster 2013 ). It should be noted that the working hypothesis and the corresponding data collection protocol does not stop inquiry and fieldwork outside the framework. The interviews could reveal an unexpected problem with Smith’s training program. The framework provides a very loose and perhaps useful ways to identify and make sense of the data that does not fit the expectations. Researchers using working hypotheses should be sensitive to interesting findings that fall outside their framework. These could be used in future studies, to refine theory or even in this case provide suggestions to improve sexual harassment training. The sensitizing concepts mentioned by Gilgun ( 2015 ) are free to emerge and should be encouraged.

Something akin to working hypotheses are hidden in plain sight in the professional literature. Take for example Kerry Crawford’s ( 2017 ) book Wartime Sexual Violence. Here she explores how basic changes in the way “advocates and decision makers think about and discuss conflict-related sexual violence” (p. 2). She focused on a subsequent shift from silence to action. The shift occurred as wartime sexual violence was reframed as a “weapon of war”. The new frame captured the attention of powerful members of the security community who demanded, initiated, and paid for institutional and policy change. Crawford ( 2017 ) examines the legacy of this key reframing. She develops a six-stage model of potential international responses to incidents of wartime violence. This model is fairly easily converted to working hypotheses and sub-hypotheses. Table  3 shows her model as a set of (non-relational) working hypotheses. She applied this model as a way to gather evidence among cases (e.g., the US response to sexual violence in the Democratic Republic of the Congo) to show the official level of response to sexual violence. Each case study chapter examined evidence to establish whether the case fit the pattern formalized in the working hypotheses. The framework was very useful in her comparative context. The framework allowed for consistent comparative analysis across cases. Her analysis of the three cases went well beyond the material covered in the framework. She freely incorporated useful inductively informed data in her analysis and discussion. The framework, however, allowed for alignment within and across cases.

9 Conclusion

In this article we argued that the exploratory research is also well suited for deductive approaches. By examining the landscape of deductive, exploratory research, we proposed the working hypothesis as a flexible conceptual framework and a useful tool for doing exploratory studies. It has the potential to guide and bring coherence across the steps in the research process. After presenting the nature of exploratory research purpose and how it differs from two types of research purposes identified in the literature—explanation, and description. We focused on answering four different questions in order to show the link between micro-conceptual frameworks and research purposes in a deductive setting. The answers to the four questions are summarized in Table  4 .

Firstly, we argued that working hypothesis and exploration are situated within the pragmatic philosophical perspective. Pragmatism allows for pluralism in theory and data collection techniques, which is compatible with the flexible exploratory purpose. Secondly, after introducing and discussing the four core elements of pragmatism (practical, pluralism, participatory, and provisional), we explained how the working hypothesis informs the methodologies and evidence collection of deductive exploratory research through a presentation of the benefits of triangulation provided by mixed methods research. Thirdly, as is clear from the article title, we introduced the working hypothesis as the micro-conceptual framework for deductive explorative research. We argued that the hypotheses of explorative research, which we call working hypotheses are distinguished from those of the explanatory research, since they do not require a relational component and are not bound by relational expectations. A working hypothesis is extremely flexible and idiosyncratic, and it could be viewed as a statement or group of statements of expectations tested in action depending on the research question. Using examples, we concluded by explaining how working hypotheses inform data collection and analysis for deductive exploratory research.

Crawford’s ( 2017 ) example showed how the structure of working hypotheses provide a framework for comparative case studies. Her criteria for analysis were specified ahead of time and used to frame each case. Thus, her comparisons were systemized across cases. Further, the framework ensured a connection between the data analysis and the literature review. Yet the flexible, working nature of the hypotheses allowed for unexpected findings to be discovered.

The evidence required to test working hypotheses is directed by the research purpose and potentially includes both quantitative and qualitative sources. Thus, all types of evidence, including quantitative methods should be part of the toolbox of deductive, explorative research. We show how the working hypotheses, as a flexible exploratory framework, resolves many seeming dualisms pervasive in the research methods literature.

To conclude, this article has provided an in-depth examination of working hypotheses taking into account philosophical questions and the larger formal research methods literature. By discussing working hypotheses as applied, theoretical tools, we demonstrated that working hypotheses fill a unique niche in the methods literature, since they provide a way to enhance alignment in deductive, explorative studies.

In practice, quantitative scholars often run multivariate analysis on data bases to find out if there are correlations. Hypotheses are tested because the statistical software does the math, not because the scholar has an a priori, relational expectation (hypothesis) well-grounded in the literature and supported by cogent arguments. Hunches are just fine. This is clearly an inductive approach to research and part of the large process of inquiry.

In 1958 , Philosophers of Science, Oppenheim and Putnam use the notion of Working Hypothesis in their title “Unity of Science as Working Hypothesis.” They too, use it as a big picture concept, “unity of science in this sense, can be fully realized constitutes an over-arching meta-scientific hypothesis, which enables one to see a unity in scientific activities that might otherwise appear disconnected or unrelated” (p. 4).

It should be noted that the positivism described in the research methods literature does not resemble philosophical positivism as developed by philosophers like Comte (Whetsell and Shields 2015 ). In the research methods literature “positivism means different things to different people….The term has long been emptied of any precise denotation …and is sometimes affixed to positions actually opposed to those espoused by the philosophers from whom the name derives” (Schrag 1992 , p. 5). For purposes of this paper, we are capturing a few essential ways positivism is presented in the research methods literature. This helps us to position the “working hypothesis” and “exploratory” research within the larger context in contemporary research methods. We are not arguing that the positivism presented here is anything more. The incompatibility theory discussed later, is an outgrowth of this research methods literature…

It should be noted that quantitative researchers often use inductive reasoning. They do this with existing data sets when they run correlations or regression analysis as a way to find relationships. They ask, what does the data tell us?

Qualitative researchers are also associated with phenomenology, hermeneutics, naturalistic inquiry and constructivism.

See Feilzer ( 2010 ), Howe ( 1988 ), Johnson and Onwuegbunzie ( 2004 ), Morgan ( 2007 ), Onwuegbuzie and Leech ( 2005 ), Biddle and Schafft ( 2015 ).

The term conceptual framework is applicable in a broad context (see Ravitch and Riggan 2012 ). The micro-conceptual framework narrows to the specific study and informs data collection (Shields and Rangarajan 2013 ; Shields et al. 2019a ) .

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Casula, M., Rangarajan, N. & Shields, P. The potential of working hypotheses for deductive exploratory research. Qual Quant 55 , 1703–1725 (2021). https://doi.org/10.1007/s11135-020-01072-9

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  • Exploratory research
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  • 1.2 The Process of Science
  • Introduction
  • 1.1 Themes and Concepts of Biology
  • Chapter Summary
  • Visual Connection Questions
  • Review Questions
  • Critical Thinking Questions
  • 2.1 The Building Blocks of Molecules
  • 2.3 Biological Molecules
  • 3.1 How Cells Are Studied
  • 3.2 Comparing Prokaryotic and Eukaryotic Cells
  • 3.3 Eukaryotic Cells
  • 3.4 The Cell Membrane
  • 3.5 Passive Transport
  • 3.6 Active Transport
  • 4.1 Energy and Metabolism
  • 4.2 Glycolysis
  • 4.3 Citric Acid Cycle and Oxidative Phosphorylation
  • 4.4 Fermentation
  • 4.5 Connections to Other Metabolic Pathways
  • 5.1 Overview of Photosynthesis
  • 5.2 The Light-Dependent Reactions of Photosynthesis
  • 5.3 The Calvin Cycle
  • 6.1 The Genome
  • 6.2 The Cell Cycle
  • 6.3 Cancer and the Cell Cycle
  • 6.4 Prokaryotic Cell Division
  • 7.1 Sexual Reproduction
  • 7.2 Meiosis
  • 7.3 Variations in Meiosis
  • 8.1 Mendel’s Experiments
  • 8.2 Laws of Inheritance
  • 8.3 Extensions of the Laws of Inheritance
  • 9.1 The Structure of DNA
  • 9.2 DNA Replication
  • 9.3 Transcription
  • 9.4 Translation
  • 9.5 How Genes Are Regulated
  • 10.1 Cloning and Genetic Engineering
  • 10.2 Biotechnology in Medicine and Agriculture
  • 10.3 Genomics and Proteomics
  • 11.1 Discovering How Populations Change
  • 11.2 Mechanisms of Evolution
  • 11.3 Evidence of Evolution
  • 11.4 Speciation
  • 11.5 Common Misconceptions about Evolution
  • 12.1 Organizing Life on Earth
  • 12.2 Determining Evolutionary Relationships
  • 13.1 Prokaryotic Diversity
  • 13.2 Eukaryotic Origins
  • 13.3 Protists
  • 14.1 The Plant Kingdom
  • 14.2 Seedless Plants
  • 14.3 Seed Plants: Gymnosperms
  • 14.4 Seed Plants: Angiosperms
  • 15.1 Features of the Animal Kingdom
  • 15.2 Sponges and Cnidarians
  • 15.3 Flatworms, Nematodes, and Arthropods
  • 15.4 Mollusks and Annelids
  • 15.5 Echinoderms and Chordates
  • 15.6 Vertebrates
  • 16.1 Homeostasis and Osmoregulation
  • 16.2 Digestive System
  • 16.3 Circulatory and Respiratory Systems
  • 16.4 Endocrine System
  • 16.5 Musculoskeletal System
  • 16.6 Nervous System
  • 17.1 Viruses
  • 17.2 Innate Immunity
  • 17.3 Adaptive Immunity
  • 17.4 Disruptions in the Immune System
  • 18.1 How Animals Reproduce
  • 18.2 Development and Organogenesis
  • 18.3 Human Reproduction
  • 19.1 Population Demographics and Dynamics
  • 19.2 Population Growth and Regulation
  • 19.3 The Human Population
  • 19.4 Community Ecology
  • 20.1 Energy Flow through Ecosystems
  • 20.2 Biogeochemical Cycles
  • 20.3 Terrestrial Biomes
  • 20.4 Aquatic and Marine Biomes
  • 21.1 Importance of Biodiversity
  • 21.2 Threats to Biodiversity
  • 21.3 Preserving Biodiversity
  • A | The Periodic Table of Elements
  • B | Geological Time
  • C | Measurements and the Metric System

Learning Objectives

  • Identify the shared characteristics of the natural sciences
  • Understand the process of scientific inquiry
  • Compare inductive reasoning with deductive reasoning
  • Describe the goals of basic science and applied science

Like geology, physics, and chemistry, biology is a science that gathers knowledge about the natural world. Specifically, biology is the study of life. The discoveries of biology are made by a community of researchers who work individually and together using agreed-on methods. In this sense, biology, like all sciences is a social enterprise like politics or the arts. The methods of science include careful observation, record keeping, logical and mathematical reasoning, experimentation, and submitting conclusions to the scrutiny of others. Science also requires considerable imagination and creativity; a well-designed experiment is commonly described as elegant, or beautiful. Like politics, science has considerable practical implications and some science is dedicated to practical applications, such as the prevention of disease (see Figure 1.15 ). Other science proceeds largely motivated by curiosity. Whatever its goal, there is no doubt that science, including biology, has transformed human existence and will continue to do so.

The Nature of Science

Biology is a science, but what exactly is science? What does the study of biology share with other scientific disciplines? Science (from the Latin scientia, meaning "knowledge") can be defined as knowledge about the natural world.

Science is a very specific way of learning, or knowing, about the world. The history of the past 500 years demonstrates that science is a very powerful way of knowing about the world; it is largely responsible for the technological revolutions that have taken place during this time. There are however, areas of knowledge and human experience that the methods of science cannot be applied to. These include such things as answering purely moral questions, aesthetic questions, or what can be generally categorized as spiritual questions. Science cannot investigate these areas because they are outside the realm of material phenomena, the phenomena of matter and energy, and cannot be observed and measured.

The scientific method is a method of research with defined steps that include experiments and careful observation. The steps of the scientific method will be examined in detail later, but one of the most important aspects of this method is the testing of hypotheses. A hypothesis is a suggested explanation for an event, which can be tested. Hypotheses, or tentative explanations, are generally produced within the context of a scientific theory . A generally accepted scientific theory is thoroughly tested and confirmed explanation for a set of observations or phenomena. Scientific theory is the foundation of scientific knowledge. In addition, in many scientific disciplines (less so in biology) there are scientific laws , often expressed in mathematical formulas, which describe how elements of nature will behave under certain specific conditions. There is not an evolution of hypotheses through theories to laws as if they represented some increase in certainty about the world. Hypotheses are the day-to-day material that scientists work with and they are developed within the context of theories. Laws are concise descriptions of parts of the world that are amenable to formulaic or mathematical description.

Natural Sciences

What would you expect to see in a museum of natural sciences? Frogs? Plants? Dinosaur skeletons? Exhibits about how the brain functions? A planetarium? Gems and minerals? Or maybe all of the above? Science includes such diverse fields as astronomy, biology, computer sciences, geology, logic, physics, chemistry, and mathematics ( Figure 1.16 ). However, those fields of science related to the physical world and its phenomena and processes are considered natural sciences . Thus, a museum of natural sciences might contain any of the items listed above.

There is no complete agreement when it comes to defining what the natural sciences include. For some experts, the natural sciences are astronomy, biology, chemistry, earth science, and physics. Other scholars choose to divide natural sciences into life sciences , which study living things and include biology, and physical sciences , which study nonliving matter and include astronomy, physics, and chemistry. Some disciplines such as biophysics and biochemistry build on two sciences and are interdisciplinary.

Scientific Inquiry

One thing is common to all forms of science: an ultimate goal “to know.” Curiosity and inquiry are the driving forces for the development of science. Scientists seek to understand the world and the way it operates. Two methods of logical thinking are used: inductive reasoning and deductive reasoning.

Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. This type of reasoning is common in descriptive science. A life scientist such as a biologist makes observations and records them. These data can be qualitative (descriptive) or quantitative (consisting of numbers), and the raw data can be supplemented with drawings, pictures, photos, or videos. From many observations, the scientist can infer conclusions (inductions) based on evidence. Inductive reasoning involves formulating generalizations inferred from careful observation and the analysis of a large amount of data. Brain studies often work this way. Many brains are observed while people are doing a task. The part of the brain that lights up, indicating activity, is then demonstrated to be the part controlling the response to that task.

Deductive reasoning or deduction is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning. Deductive reasoning is a form of logical thinking that uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid. For example, a prediction would be that if the climate is becoming warmer in a region, the distribution of plants and animals should change. Comparisons have been made between distributions in the past and the present, and the many changes that have been found are consistent with a warming climate. Finding the change in distribution is evidence that the climate change conclusion is a valid one.

Both types of logical thinking are related to the two main pathways of scientific study: descriptive science and hypothesis-based science. Descriptive (or discovery) science aims to observe, explore, and discover, while hypothesis-based science begins with a specific question or problem and a potential answer or solution that can be tested. The boundary between these two forms of study is often blurred, because most scientific endeavors combine both approaches. Observations lead to questions, questions lead to forming a hypothesis as a possible answer to those questions, and then the hypothesis is tested. Thus, descriptive science and hypothesis-based science are in continuous dialogue.

Hypothesis Testing

Biologists study the living world by posing questions about it and seeking science-based responses. This approach is common to other sciences as well and is often referred to as the scientific method. The scientific method was used even in ancient times, but it was first documented by England’s Sir Francis Bacon (1561–1626) ( Figure 1.17 ), who set up inductive methods for scientific inquiry. The scientific method is not exclusively used by biologists but can be applied to almost anything as a logical problem-solving method.

The scientific process typically starts with an observation (often a problem to be solved) that leads to a question. Let’s think about a simple problem that starts with an observation and apply the scientific method to solve the problem. One Monday morning, a student arrives at class and quickly discovers that the classroom is too warm. That is an observation that also describes a problem: the classroom is too warm. The student then asks a question: “Why is the classroom so warm?”

Recall that a hypothesis is a suggested explanation that can be tested. To solve a problem, several hypotheses may be proposed. For example, one hypothesis might be, “The classroom is warm because no one turned on the air conditioning.” But there could be other responses to the question, and therefore other hypotheses may be proposed. A second hypothesis might be, “The classroom is warm because there is a power failure, and so the air conditioning doesn’t work.”

Once a hypothesis has been selected, a prediction may be made. A prediction is similar to a hypothesis but it typically has the format “If . . . then . . . .” For example, the prediction for the first hypothesis might be, “ If the student turns on the air conditioning, then the classroom will no longer be too warm.”

A hypothesis must be testable to ensure that it is valid. For example, a hypothesis that depends on what a bear thinks is not testable, because it can never be known what a bear thinks. It should also be falsifiable , meaning that it can be disproven by experimental results. An example of an unfalsifiable hypothesis is “Botticelli’s Birth of Venus is beautiful.” There is no experiment that might show this statement to be false. To test a hypothesis, a researcher will conduct one or more experiments designed to eliminate one or more of the hypotheses. This is important. A hypothesis can be disproven, or eliminated, but it can never be proven. Science does not deal in proofs like mathematics. If an experiment fails to disprove a hypothesis, then we find support for that explanation, but this is not to say that down the road a better explanation will not be found, or a more carefully designed experiment will be found to falsify the hypothesis.

Each experiment will have one or more variables and one or more controls. A variable is any part of the experiment that can vary or change during the experiment. A control is a part of the experiment that does not change. Look for the variables and controls in the example that follows. As a simple example, an experiment might be conducted to test the hypothesis that phosphate limits the growth of algae in freshwater ponds. A series of artificial ponds are filled with water and half of them are treated by adding phosphate each week, while the other half are treated by adding a salt that is known not to be used by algae. The variable here is the phosphate (or lack of phosphate), the experimental or treatment cases are the ponds with added phosphate and the control ponds are those with something inert added, such as the salt. Just adding something is also a control against the possibility that adding extra matter to the pond has an effect. If the treated ponds show lesser growth of algae, then we have found support for our hypothesis. If they do not, then we reject our hypothesis. Be aware that rejecting one hypothesis does not determine whether or not the other hypotheses can be accepted; it simply eliminates one hypothesis that is not valid ( Figure 1.18 ). Using the scientific method, the hypotheses that are inconsistent with experimental data are rejected.

In recent years a new approach of testing hypotheses has developed as a result of an exponential growth of data deposited in various databases. Using computer algorithms and statistical analyses of data in databases, a new field of so-called "data research" (also referred to as "in silico" research) provides new methods of data analyses and their interpretation. This will increase the demand for specialists in both biology and computer science, a promising career opportunity.

Visual Connection

In the example below, the scientific method is used to solve an everyday problem. Which part in the example below is the hypothesis? Which is the prediction? Based on the results of the experiment, is the hypothesis supported? If it is not supported, propose some alternative hypotheses.

  • My toaster doesn’t toast my bread.
  • Why doesn’t my toaster work?
  • There is something wrong with the electrical outlet.
  • If something is wrong with the outlet, my coffeemaker also won’t work when plugged into it.
  • I plug my coffeemaker into the outlet.
  • My coffeemaker works.

In practice, the scientific method is not as rigid and structured as it might at first appear. Sometimes an experiment leads to conclusions that favor a change in approach; often, an experiment brings entirely new scientific questions to the puzzle. Many times, science does not operate in a linear fashion; instead, scientists continually draw inferences and make generalizations, finding patterns as their research proceeds. Scientific reasoning is more complex than the scientific method alone suggests.

Basic and Applied Science

The scientific community has been debating for the last few decades about the value of different types of science. Is it valuable to pursue science for the sake of simply gaining knowledge, or does scientific knowledge only have worth if we can apply it to solving a specific problem or bettering our lives? This question focuses on the differences between two types of science: basic science and applied science.

Basic science or “pure” science seeks to expand knowledge regardless of the short-term application of that knowledge. It is not focused on developing a product or a service of immediate public or commercial value. The immediate goal of basic science is knowledge for knowledge’s sake, though this does not mean that in the end it may not result in an application.

In contrast, applied science or “technology,” aims to use science to solve real-world problems, making it possible, for example, to improve a crop yield, find a cure for a particular disease, or save animals threatened by a natural disaster. In applied science, the problem is usually defined for the researcher.

Some individuals may perceive applied science as “useful” and basic science as “useless.” A question these people might pose to a scientist advocating knowledge acquisition would be, “What for?” A careful look at the history of science, however, reveals that basic knowledge has resulted in many remarkable applications of great value. Many scientists think that a basic understanding of science is necessary before an application is developed; therefore, applied science relies on the results generated through basic science. Other scientists think that it is time to move on from basic science and instead to find solutions to actual problems. Both approaches are valid. It is true that there are problems that demand immediate attention; however, few solutions would be found without the help of the knowledge generated through basic science.

One example of how basic and applied science can work together to solve practical problems occurred after the discovery of DNA structure led to an understanding of the molecular mechanisms governing DNA replication. Strands of DNA, unique in every human, are found in our cells, where they provide the instructions necessary for life. During DNA replication, new copies of DNA are made, shortly before a cell divides to form new cells. Understanding the mechanisms of DNA replication enabled scientists to develop laboratory techniques that are now used to identify genetic diseases, pinpoint individuals who were at a crime scene, and determine paternity. Without basic science, it is unlikely that applied science could exist.

Another example of the link between basic and applied research is the Human Genome Project, a study in which each human chromosome was analyzed and mapped to determine the precise sequence of DNA subunits and the exact location of each gene. (The gene is the basic unit of heredity represented by a specific DNA segment that codes for a functional molecule.) Other organisms have also been studied as part of this project to gain a better understanding of human chromosomes. The Human Genome Project ( Figure 1.19 ) relied on basic research carried out with non-human organisms and, later, with the human genome. An important end goal eventually became using the data for applied research seeking cures for genetically related diseases.

While research efforts in both basic science and applied science are usually carefully planned, it is important to note that some discoveries are made by serendipity, that is, by means of a fortunate accident or a lucky surprise. Penicillin was discovered when biologist Alexander Fleming accidentally left a petri dish of Staphylococcus bacteria open. An unwanted mold grew, killing the bacteria. The mold turned out to be Penicillium , and a new critically important antibiotic was discovered. In a similar manner, Percy Lavon Julian was an established medicinal chemist working on a way to mass produce compounds with which to manufacture important drugs. He was focused on using soybean oil in the production of progesterone (a hormone important in the menstrual cycle and pregnancy), but it wasn't until water accidentally leaked into a large soybean oil storage tank that he found his method. Immediately recognizing the resulting substance as stigmasterol, a primary ingredient in progesterone and similar drugs, he began the process of replicating and industrializing the process in a manner that has helped millions of people. Even in the highly organized world of science, luck—when combined with an observant, curious mind focused on the types of reasoning discussed above—can lead to unexpected breakthroughs.

Reporting Scientific Work

Whether scientific research is basic science or applied science, scientists must share their findings for other researchers to expand and build upon their discoveries. Communication and collaboration within and between sub disciplines of science are key to the advancement of knowledge in science. For this reason, an important aspect of a scientist’s work is disseminating results and communicating with peers. Scientists can share results by presenting them at a scientific meeting or conference, but this approach can reach only the limited few who are present. Instead, most scientists present their results in peer-reviewed articles that are published in scientific journals. Peer-reviewed articles are scientific papers that are reviewed, usually anonymously by a scientist’s colleagues, or peers. These colleagues are qualified individuals, often experts in the same research area, who judge whether or not the scientist’s work is suitable for publication. The process of peer review helps to ensure that the research described in a scientific paper or grant proposal is original, significant, logical, and thorough. Grant proposals, which are requests for research funding, are also subject to peer review. Scientists publish their work so other scientists can reproduce their experiments under similar or different conditions to expand on the findings.

There are many journals and the popular press that do not use a peer-review system. A large number of online open-access journals, journals with articles available without cost, are now available many of which use rigorous peer-review systems, but some of which do not. Results of any studies published in these forums without peer review are not reliable and should not form the basis for other scientific work. In one exception, journals may allow a researcher to cite a personal communication from another researcher about unpublished results with the cited author’s permission.

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  • What Is Deductive Reasoning? | Explanation & Examples

What Is Deductive Reasoning? | Explanation & Examples

Published on 4 May 2022 by Pritha Bhandari . Revised on 5 December 2022.

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 or top-down reasoning.

Deductive-reasoning

Table of contents

What is deductive reasoning, validity and soundness, deductive reasoning in research, deductive vs inductive reasoning, frequently asked questions about deductive reasoning.

In deductive reasoning, you’ll often make an argument for a certain idea. You make an inference, or come to a conclusion, by applying different premises.

A premise is a generally accepted idea, fact, or rule, and it’s a statement that lays the groundwork for a theory or general idea. Conclusions are statements supported by premises.

Deductive logic arguments

In a simple deductive logic argument, you’ll often begin with a premise, and add another premise. Then, you form a conclusion based on these two premises. This format is called ‘premise-premise-conclusion’.

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Validity and soundness are two criteria for assessing deductive reasoning arguments.

In this context, validity is about the way the premises relate to each other and the conclusion. This is a different concept from research validity .

An argument is valid if the premises logically support and relate to the conclusion. But the premises don’t need to be true for an argument to be valid.

  • If there’s a rainbow, flights get cancelled.
  • There is a rainbow now.
  • Therefore, flights are cancelled.
  • All chili peppers are spicy.
  • Tomatoes are a chili pepper.
  • Therefore, tomatoes are spicy.

In an invalid argument, your premises can be true but that doesn’t guarantee a true conclusion. Your conclusion may inadvertently be true, but your argument can still be invalid because your conclusion doesn’t logically follow from the relationship between the statements.

  • All leopards have spots.
  • My pet gecko has spots.
  • Therefore, my pet gecko is a leopard.
  • All US presidents live in the White House.
  • Barack Obama lived in the White House.
  • Therefore, Barack Obama was a US president.

An argument is sound only if it’s valid and the premises are true. All invalid arguments are unsound.

If you begin with true premises and a valid argument, you’re bound to come to a true conclusion.

  • Flights get cancelled when there are extreme weather conditions.
  • There are extreme weather conditions right now.
  • All fruits are grown from flowers and contain seeds.
  • Tomatoes are grown from flowers and contain seeds.
  • Therefore, tomatoes are fruits.

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

This method is used for academic as well as non-academic research.

Here are the general steps for deductive research:

  • Select a research problem and create a problem statement.
  • Develop falsifiable hypotheses .
  • Collect your data with appropriate measures.
  • Analyse and test your data.
  • Decide whether to reject your null hypothesis .

Importantly, your hypotheses should be falsifiable. If they aren’t, you won’t be able to determine whether your results support them or not.

You formulate your main hypothesis: Switching to a four-day work week will improve employee well-being. Your null hypothesis states that there’ll be no difference in employee well-being before and after the change.

You collect data on employee well-being through quantitative surveys on a monthly basis before and after the change. When analysing the data, you note a 25% increase in employee well-being after the change in work week.

Deductive reasoning is a top-down approach, while inductive reasoning is a bottom-up approach.

In deductive reasoning, you start with general ideas and work toward specific conclusions through inferences. Based on theories, you form a hypothesis. Using empirical observations, you test that hypothesis using inferential statistics and form a conclusion.

Inductive reasoning is also called a hypothesis-generating approach, because you start with specific observations and build toward a theory. It’s an exploratory method that’s often applied before deductive research.

In practice, most research projects involve both inductive and deductive methods.

Deductive reasoning is also called deductive logic.

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.

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8.1: Hypothetical Reasoning

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Suppose I’m going on a picnic and I’m only selecting items that fit a certain rule. You want to find out what rule I’m using, so you offer up some guesses at items I might want to bring:

An Egg Salad Sandwich

A grape soda

Suppose now that I tell you that I’m okay with the first two, but I won’t bring the third. Your next step is interesting: you look at the first two, figure out what they have in common, and then you take a guess at the rule I’m using. In other words, you posit a hypothesis. You say something like

Do you only want to bring things that are yellow or tan?

Notice how at this point your hypothesis goes way beyond the evidence. Bananas and egg salad sandwiches have so much more in common than being yellow/tan objects. This is how hypothetical reasoning works: you look at the evidence, add a hypothesis that makes sense of that evidence (one among many hypotheses available), and then check to be sure that your hypothesis continues to make sense of new evidence as it is collected.

Suppose I now tell you that you haven’t guessed the right rule. So, you might throw out some more objects:

A key lime pie

A jug of orange juice

I then tell you that the first two are okay, but again the last item is not going with me on this picnic.

It’s solid items! Solid items are okay, but liquid items are not.

Again, not quite. Try another set of items. You are still convinced that it has to do with the soda and the juice being liquid, so you try out an interesting tactic:

An ice cube

Some liquid water

Some water Vapor

The first and last items are okay, but not the middle one. Now you think you’ve got me. You guess that the rule is “anything but liquids,” but I refuse to tell you whether you got it right. You’re pretty confident at this point, but perhaps you’re not certain . In principle, there could always be more evidence that upsets your hypothesis. I might say that the ocean is okay but a fresh water lake isn’t, and that would be very confusing for you. You’ll never be quite certain that you’ve guessed my rule correctly because it’s always in principle possible that I have a super complex rule that is more complex than your hypothesis.

So in hypothetical reasoning what we’re doing is making a leap from the evidence we have available to the rule or principle or theory which explains that evidence. The hypothesis is the link between the two. We have some finite evidence available to us, and we hypothesize an explanation. The explanation we posit either is or is not the true explanation, and so we’re using the hypothesis as a bridge to get onto the true explanation of what is happening in the world.

The hypothetical method has four stages. Let’s illustrate each with an example. You are investigating a murder and have collected a lot of evidence but do not yet have a guess as to who the killer might be.

1. The occurrence of a problem

Example \(\PageIndex{1}\)

Someone has been murdered and we need to find out who the killer is so that we might bring them to justice.

2. Formulating a hypothesis

Example \(\PageIndex{2}\)

After collecting some evidence, you weigh the reasons in favor of thinking that each suspect is indeed the murderer, and you decide that the spouse is responsible.

3. Drawing implications from the hypothesis

Example \(\PageIndex{3}\)

If the spouse was the murderer, then a number of things follow. The spouse must have a weak alibi or their alibi must rest on some falsehood. There is likely to be some evidence on their property or among their belongings that links the spouse to the murder. The spouse likely had motive. etc., etc., etc.

We can go on for ages, but the basic point is that once we’ve got an idea of what the explanation for the murder is (in this case, the hypothesis is that the spouse murdered the victim), we can ask ourselves what the world would have to be like for that to have been true. Then we move onto the final step:

4. Test those implications.

Example \(\PageIndex{4}\)

We can search the murder scene, try to find a murder weapon, run DNA analysis on the organic matter left at the scene, question the spouse about their alibi and possible motives, check their bank accounts, talk to friends and neighbors, etc. Once we have a hypothesis, in other words, that hypothesis drives the search for new evidence—it tells us what might be relevant and what irrelevant and therefore what is worth our time and what is not.

The Logic of Hypothetical Reasoning

If the spouse did it, then they must have a weak alibi. Their alibi is only verifiable by one person: the victim. So they do have a weak alibi. Therefore...they did it? Not quite.

Just because they have a weak alibi doesn’t mean they did it. If that were true, anyone with a weak alibi would be guilty for everything bad that happened when they weren’t busy with a verifiable activity.

Similarly, if your car’s battery is dead, then it won’t start. This doesn’t mean that whenever your car doesn’t start, the battery is dead. That would be a wild and bananas claim to make (and obviously false), but the original conditional (the first sentence in this paragraph) isn’t wild and bananas. In fact, it’s a pretty normal claim to make and it seems obviously true.

Let’s talk briefly about the logic of hypothetical reasoning so we can discover an important truth.

If the spouse did it, then their alibi will be weak

Their alibi is weak

So, the spouse did it

This is bad reasoning. How do we know? Well, here’s the logical form:

If A, then B

Therefore, A

This argument structure—called “affirming the consequent”—is invalid because there are countless instances of this general structure that have true premises and a false conclusion. Consider the following examples:

Example \(\PageIndex{5}\)

If I cook, I eat well

I ate well tonight, so I cooked.

Example \(\PageIndex{6}\)

If Eric runs for student president, he’ll become more popular.

Eric did become more popular, so he must’ve run for student president.

Maybe I ate well because I’m at the finest restaurant in town. Maybe I ate well because my brother cooked for me. Any of these things is possible, which is the root problem with this argument structure. It infers that one of the many possible antecedents to the conditional is the true antecedent without giving any reason for choosing or preferring this antecedent.

More concretely, affirming the consequent is the structure of an argument that states that a) one thing will explain an event, and b) that the event in question in fact occurred, and then concludes that c) the one thing that would’ve explained the event is the correct explanation of the event.

More concretely still, here’s yet another example of affirming the consequent:

Example \(\PageIndex{7}\)

My being rich would explain my being popular

I am in fact popular,

Therefore I am in fact rich

I might be popular without having a penny to my name. People sometimes root for underdogs, or respond to the right kind of personality regardless of their socioeconomic standing, or respect a good sense of humor or athletic prowess.

If I were rich, though, that would be one potential explanation for my being popular. Rich people have nice clothes, cool cars, nice houses, and get to have the kinds of experiences that make someone a potentially popular person because everyone wants to hear the cool stories or be associated with the exciting life they lead. Perhaps, people often seem to think, they’ll get to participate in the next adventure if they cozy up to the rich people. Rich kids in high school can also throw the best parties (if we’re honest, and that’s a great source of popularity).

But If I’m not rich, that doesn’t mean I’m not popular. It only means that I’m not popular because I’m rich .

Okay, so we’ve established that hypothetical reasoning has the logical structure of affirming the consequent. We’ve further established that affirming the consequent is an invalid deductive argumentative structure. Where does this leave us? Is the hypothetical method bad reasoning ?!?!?!? Nope! Luckily not all reasoning is deductive reasoning.

Remember that we’re discussing inductive reasoning in this chapter. Inductive reasoning doesn’t obey the rules of deductive logic. So it’s no crime for a method of inductive reasoning to be deductively invalid. The crime against logic would be to claim that we have certain knowledge when we only use inductive reasoning to justify that knowledge. The upshot? Science doesn’t produce certain knowledge—it produces justified knowledge, knowledge to a more or less high degree of certitude, knowledge that we can rely on and build bridges on, knowledge that almost certainly won’t let us down (but it doesn’t produce certain knowledge).

We can, though, with deductive certainty, falsify a hypothesis. Consider the murder case: if the spouse did it, then they’d have a weak alibi. That is, if the spouse did it, then they wouldn’t have an airtight alibi because they’d have to be lying about where they were when the murder took place. If it turns out that the spouse does have an airtight alibi, then your hypothesis was wrong.

Let’s take a look at the logic of falsification:

If the spouse did it, then they won’t have an airtight alibi

They have an airtight alibi

So the spouse didn’t do it

Now it’s possible that the conditional premise (the first premise) isn’t true, but we’ll assume it’s true for the sake of the illustration. The hypothesis was that the spouse did it and so the spouse’s alibi must have some weakness.

It’s also possible that our detective work hasn’t been thorough enough and so the second premise is false. These are important possibilities to keep in mind. Either way, here’s the logical form (a bit cleaned up and simplified):

Therefore not A

This is what argument pattern? That’s right! You’re so smart! It’s modus tollens or “the method of denying”. It’s a type of argument where you deny the implications of something and thereby deny that very thing. It’s a deductively valid argument form (remember from our unit on natural deduction?), so we can falsify hypotheses with deductive certainty: if your hypothesis implies something with necessity, and that something doesn’t come to pass, then your hypothesis is wrong.

Your hypothesis is wrong. That is, your hypothesis as it stands was wrong. You might be like one of those rogue and dogged detectives in the television shows that never gives up on a hunch and ultimately discovers the truth through sheer stubbornness and determination. You might think that the spouse did it, even though they’ve got an airtight alibi. In that case, you’ll have to alter your hypothesis a bit.

The process of altering a hypothesis to react to potentially falsifying evidence typically involves adding extra hypotheses onto your original hypothesis such that the original hypothesis no longer has the troubling implications which turned out not to be true. These extra hypotheses are called ad hoc hypotheses.

As an example, Newton’s theory of gravity had one problem: it made a sort of wacky prediction. So the idea was that gravity was an instantaneous attractive force exerted by all massive bodies on all other bodies. That is, all bodies attract all other bodies regardless of distance or time. The result of this should be that all massive bodies should smack into each other over time (after all, they still have to travel towards one another). But we don’t witness this. We should see things crashing towards the center of gravity of the universe at incredible speeds, but that’s not what’s happening. So, by the logic of falsification, Newton’s theory is simply false.

But Newton had a trick up his sleeve: he claimed that God arranged things such that the heavenly bodies are so far apart from one another that they are prevented from crashing into one another. Problem solved! God put things in the right spatial orientation such that the theory of gravity is saved: they won’t crash into each other because they’re so far apart! Newton employed an ad hoc hypothesis to save his theory from falsification.

Abductive Reasoning

There’s one more thing to discuss while we’re still on the topic of hypothetical reasoning or reasoning using hypotheses. ‘Abduction’ is a fancy word for a process or method sometimes called “inference to the best explanation. The basic idea is that we have a bunch of evidence, we try to explain it, and we find that we could explain it in multiple ways. Then we find the “best” explanation or hypothesis and infer that this is the true explanation.

For example, say we’re playing a game that’s sort of like the picnic game from before. I give you a series of numbers, and then you give me more series of numbers so that I can confirm or deny that each meets the rule I have in mind. So I say:

And then you offer the following series (serieses?):

60, 90, 120

Each of these series tests a particular hypothesis. The first tests whether the important thing is that the numbers start with 2, 3, and 4. The second tests whether the rule is to add 10 each successive number in the series. The third tests a more complicated hypothesis: add half of the first number to itself to get the second number, then add one third of the second number to itself to get the third number.

Now let’s say I tell you that only the third series is acceptable. What now?

Well, our hypothesis was pretty complex, but it seems pretty good. I can infer that this is the correct rule. Alternatively, I might look at other hypotheses which fit the evidence equally well: 1x, 1.5x, 2x? or maybe it’s 2x, 3x, 4x? What about x, 1.5x, x\(^2\)? These all make sense of the data, but are they equal apart from that?

Let’s suppose we can’t easily get more data with which to test our various hypotheses. We’ve got 4 to choose from and nothing in the evidence suggests that one of the hypotheses is better than the others—they all fit the evidence perfectly. What do we do?

One thing we could do is choose which hypothesis is best for reasons other than fit with the evidence. Maybe we want a simpler hypothesis, or maybe we want a more elegant hypothesis, or one which suggests more routes for investigation. These are what we might call “theoretical virtues”—they’re the things we want to see in a theory. The process of abduction is the process of selecting the hypothesis that has the most to offer in terms of theoretical virtues: the simplest, most elegant, most fruitful, most general, and so on.

In science in particular, we value a few theoretical virtues over others: support by the empirical evidence available, replicability of the results in a controlled setting by other scientists, ideally mathematical precision or at least a lack of vagueness, and parsimony or simplicity in terms of the sorts of things the hypothesis requires us to believe in.

Confirmation Bias

This is a great opportunity to discuss confirmation bias, or the natural tendency we have to seek out evidence which supports our beliefs and to ignore evidence which gets in the way of our beliefs. We’ll discuss cognitive biases more in Chapter 10, but since we’re dealing with the relationship between evidence and belief, this seems like a good spot to pause and reflect on how our minds work.

The way our minds work naturally, it seems, is to settle on a belief and then work hard to maintain that belief whatever happens. We come to believe that global warming is anthropogenic—is caused by human activities—and then we’re happy to accept a wide variety of evidence for the claim. If the evidence supports our belief, in other words, we don’t take the time or energy to really investigate exactly how convincing that evidence is. If we already believe the conclusion of an inference, in other words, we are much less likely to test or analyze the inference.

Alternatively, when we see pieces of evidence or arguments that appear to point to the contrary, we are either more skeptical of that evidence or more critical of that argument. For instance, if someone notes that the Earth goes through normal cycles of warming and ice ages and warming again, we immediately will look for ways to explain how this warming period is different than others in the past. Or we might look at the period of the cycles to find out if this is happening at the “right” time in our geological history for it not to be caused by humankind. In other words, we’re more skeptical of arguments or evidence that would defeat or undermine our beliefs, but we’re less skeptical and critical of arguments and evidence that supports our beliefs.

Here are some questions to reflect on as you try to decide how guilty you are of confirmation bias in your own reasoning:

Questions for Reflection:

1. Which news sources do you trust? Why?

2. What’s your process for exploring a topic—say a political or scientific or news topic?

3. How do you decide what to believe about a new subject?

4. When other people express an opinion about someone you don’t know, do you withhold judgment? How well do you do so?

5. Are you harder on arguments and evidence that would shake up your beliefs?

Deductive Reasoning (Definition + Examples)

December 6, 2022

At the age of 11 or 12, children reach what famed psychologist Jean Piaget called the formal operational stage. It’s the final stage in the child’s development. During this stage, children start to think abstractly and even apply those ideas to problem-solving. They also learn a process called deductive reasoning. 

Deductive reasoning is a process in which we draw conclusions about the world around us. It’s also one of the basic ideas introduced to students who are learning about logic and how to form an argument. Deductive reasoning can help us discover the truth, but as you’ll see in the video, sometimes this process is done so quickly because it’s obvious.

On this page, I’m going to talk about deductive reasoning, how we use it in everyday life, and how it differs from inductive reasoning. Understanding deductive and inductive reasoning are essential building blocks for understanding how we make sense of the world and how we make decisions.

deductive reasoning and inductive reasoning

What is Deductive Reasoning?

Deductive reasoning, or deduction, is the process of using a group of true premises to draw a conclusion that is also true. This is also known as “top-down logic” because it takes broad statements and uses them to create more narrow statements.

Here’s an example of deductive reasoning.

Premise A says that all dogs are good boys.

Premise B says that Kevin is a dog.

The conclusion that we draw from deductive reasoning says that Kevin is a good boy.

Of course, that example is just silly, but it shows how we can use two ideas and deductive reasoning to form an argument or a statement. Other examples of premises like this include “all dogs are mammals” or “every human embryo is made from sperm and an egg.”

Premise A is typically a very broad and general statement. Premise B is a more narrow statement that relates back to Premise A. The conclusion states a narrow truth that relates to both Premise A and Premise B.

Characteristics of deductive reasoning

In order to start the deductive reasoning process, you must use a statement that we all know to be true. If the statement is not true, or true some of the time, you may still be able to form a conclusion through induction. But in order to use deductive reasoning, that truth needs to be as solid as concrete.

It will also have to funnel down to make a more narrow conclusion through entailment. Premise A and Premise B must be related in a way that Premise C can exist. Let’s go back to our example.

In both Premise A and Premise B, dogs are mentioned. Premise C grabs a conclusion from both of these premises in a logical, relevant way. When any of these parts of the deduction don’t follow the rules, problems may ensue.

steps of deductive reasoning

The rules of deductive reasoning are airtight. If you’re not following them, you’re not using deductive reasoning. This may not change the validity of the premises or the conclusions that you draw from your premises, but it does change whether or not it falls under the category of deductive reasoning.

If any of the following exist, you might end up coming to a false conclusion:

  • False premises
  • Lack of entailment
  • A narrow truth

False Premises

Let’s go back to the idea that all dogs are good boys. In this case, one can unfortunately argue that not all dogs are good boys. This would automatically make the conclusion untrue. A conclusion is only considered the truth when the premises that precede it are true.

Notice here that we said that the conclusion is untrue. You may come back and argue that Kevin is a good boy, even though not all dogs are. That simply means that the conclusion is valid. In philosophy, validity and truth are not the same thing.

So while some dogs are good boys, Kevin is a dog, and Kevin is a good boy, this is not a conclusion that you can draw through deductive reasoning as it was laid out by ancient philosophers.

Lack of Entailment

Kevin is a good boy (as discovered by deductive reasoning)

Here’s another problem with deductive reasoning that we run into a lot. In order for a conclusion to be true, the premises that precede it directly support and lead to the conclusion.

Here’s an example of how failing to use this rule can create a weak conclusion. (Let’s go back to pretending that “all dogs are good boys” is a known fact.)

The conclusion drawn from this is that Kevin has blue eyes.

Kevin could very well have blue eyes, but just because the conclusion is valid doesn’t mean that it is true, because we have nothing to support the idea that Kevin’s eyes are blue.

Remember, you have to reach this conclusion through entailment. No premise has anything to do with the color of Kevin’s eyes or the color of any dog’s eyes. So we can’t come to that conclusion based on the premises that have been given to us.

Narrow Truth

Think of all of the things that you know as true. Surprisingly, these broad and general facts are not easy to come by. And when they do, they always seem too obvious to use in an example.

So deductive reasoning also seems very obvious, and outside from being the basis of forming an argument, it’s not useful in everyday life.

Let’s use another example of deductive reasoning, shall we?

Premise A says that all humans live on land.

Premise B says that Megan is a human.

The conclusion that you would get from deductive reasoning says that Megan lives on land.

Well, yeah. Duh. She’s a human, after all.

Deductive reasoning comes naturally to us. We do it without thinking. To figure out that a human lives on land or that a dog is a mammal is a quick process when you already know that all dogs are mammals and that all humans live on land.

But due to the nature of deductive reasoning, you need those broad truths to draw conclusions from. A more narrow truth won’t give you much to work with.

Example 1: All humans are mortal. Susan is a human. Susan is mortal.

This is a classic example of deductive reasoning. It starts with a statement that is entirely true – you can’t poke holes in it or argue against it. (Maybe in a few decades you can, but not today!) The next statement is also true, and ties into the first statement. The conclusion brings both statements together to create a statement that we have now proven is true.

Example 2: Marketing

In everyday life, we don’t always use deductive reasoning using the strict rules of traditional logic. Marketers, for example, may use deductive reasoning to make decisions about how they want to advertise their products toward certain groups of customers.

They may use information from focus groups or surveys to create a profile of their products. Let’s say a company that makes cleaning products wants to target single women, in their late 20s, who are upper-middle-class. They collect information about the demographic and learn that upper-middle-class single women in their late 20s find more value products that have natural ingredients and are “green.”

Premise 1 is that upper-middle-class women in their 20s find more value in products that have natural ingredients and are “green.”

Premise 2 is that the company’s target audience is upper-middle class women in their 20s.

The marketers draw a conclusion that if they brand their products as “green” and highlight their natural ingredients, their target audience will find more value in their products.

Again, this doesn’t exactly fit the rules of “top-down logic.” Not every upper-middle-class woman particularly cares what is in their cleaning products. And not every upper-middle-class woman is in the company’s target audience. But this is often how we use deductive reasoning to draw conclusions. These conclusions can still be very helpful, even if the conclusions aren’t 100% true.

Example 3: Deductive Reasoning in Math

Deductive reasoning is introduced in math classes to help students understand equations and create proofs. When math teachers discuss deductive reasoning, they usually talk about syllogisms. Syllogisms are a form of deductive reasoning that help people discover a truth.

Here’s an example.

The sum of any triangle’s three angles is 180 degrees.

You are given a triangle to work with.

You can conclude that the sum of the triangle’s three angles is 180 degrees.

This conclusion will help you move forward when working with the triangle and discovering the length of each side or the measurement of each angle.

Example 4: Deductive Reasoning in Science

Both deduction and induction are used to prove hypotheses and support the scientific method. Deduction requires us to look at how closely a premises and the conclusion are related. If the premises are backed by evidence and experiment, then the conclusion is more likely to be true.

In the scientific method, scientists form a hypothesis. They then conduct a series of experiments to see whether that hypothesis is true. With each experiment, they prove the strength of the premises and support their conclusion about whether or not their hypothesis is correct.

Without deductive reasoning, scientists may come to untrue conclusions or accept things that are likely as things that are true.

Deductive vs inductive reasoning

In the beginning of this video, I mentioned that child psychologist Jean Piaget theorized that children developed the skills of deductive reasoning around 11 or 12 years old. From then on, it’s not exactly something that we think about.

So we’re more likely to draw conclusions about things in the opposite direction. We use inductive reasoning to make sense of the world around us. We take a single experience or a few experiences from the past to make a conclusion about what might happen in the immediate future or indefinitely.

Inductive reasoning is more prevalent in our everyday lives because it just requires a personal experience or a handful of facts to work. Getting down to the “truth,” especially if you are a philosopher or someone who is especially skilled in logic, is not always an easy thing to do. Plus, using deductive reasoning doesn’t usually give us any incentive or confidence to take action. It just helps us build the world.

But I’ll talk more about inductive reasoning in my next video. I’ll break down what inductive reasoning is, the different types of inductive reasoning that we use in everyday life, and the problems that come with inductive reasoning.

Have you been listening? Let’s test your knowledge with a quick, three-question quiz on deductive reasoning.

First question:

Is deductive reasoning considered “top-down” or “bottom-up” logic?

“Top-down logic.” It starts with broad truths and makes its way down to a more narrow conclusion. “Bottom-up logic” is called induction. 

Second question:

What can interfere with deduction?

A: False premises

B: Lack of entailment

C: Narrow truth

D: All of the above

All of the above! In order to arrive at the truth, you will need to provide true premises, that logically lead to the conclusion. This means starting with a very broad truth and making your way down.

Last question: does this “count” as deductive reasoning?

Premise 1: All pigeons are birds.

Premise 2: John is a pigeon.

Conclusion: John is a bird.

Yes, it counts! All of the premises are true and contribute to the final conclusion, which is also true.

Related posts:

  • Inductive Reasoning (Definition + Examples)
  • Perceptual Reasoning (Definition + Examples)
  • Formal Operational Stage
  • Robbers Cave Experiment
  • Ivan Pavlov (Biography + Experiments)

forming a hypothesis is accomplished through deductive reasoning

Theodore T.

Theodore is a professional psychology educator with over 10 years of experience creating educational content on the internet. PracticalPsychology started as a helpful collection of psychological articles to help other students, which has expanded to a Youtube channel with over 2,000,000 subscribers and an online website with 500+ posts.

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Chapter 3: Developmental Theories

Understanding Theories

Scientific knowledge is advanced through a process known as the scientific method. Basically, ideas (in the form of theories and hypotheses) are tested against the real world (in the form of empirical observations), and those empirical observations lead to more ideas that are tested against the real world, and so on. In this sense, the scientific process is circular. The types of reasoning within the circle are called deductive and inductive. In deductive reasoning , ideas are tested against the empirical world; in inductive reasoning , empirical observations lead to new ideas (Figure 3.2). These processes are inseparable, like inhaling and exhaling, but different research approaches place different emphasis on the deductive and inductive aspects.

forming a hypothesis is accomplished through deductive reasoning

Figure 3.2  Psychological research relies on both inductive and deductive reasoning.

In the scientific context, deductive reasoning begins with a generalization—one hypothesis—that is then used to reach logical conclusions about the real world. If the hypothesis is correct, then the logical conclusions reached through deductive reasoning should also be correct. A deductive reasoning argument might go something like this: All living things require energy to survive (this would be your hypothesis). Ducks are living things. Therefore, ducks require energy to survive (logical conclusion). In this example, the hypothesis is correct; therefore, the conclusion is correct as well. Sometimes, however, an incorrect hypothesis may lead to a logical but incorrect conclusion. Consider this argument: all ducks are born with the ability to see. Quackers is a duck. Therefore, Quackers was born with the ability to see. Scientists use deductive reasoning to test their hypotheses empirically. Returning to the example of the ducks, researchers might design a study to test the hypothesis that if all living things require energy to survive, then ducks will be found to require energy to survive.

Deductive reasoning starts with a generalization that is tested against real-world observations; however, inductive reasoning moves in the opposite direction. Inductive reasoning uses empirical observations to construct broad generalizations. Unlike deductive reasoning, conclusions drawn from inductive reasoning may or may not be correct, regardless of the observations on which they are based. For instance, you may notice that your favorite fruits—apples, bananas, and oranges—all grow on trees; therefore, you assume that all fruit must grow on trees. This would be an example of inductive reasoning, and the existence of strawberries, blueberries, and kiwi demonstrates that this generalization is not correct despite it being based on several direct observations. Scientists use inductive reasoning to formulate theories, which in turn generate hypotheses that are tested with deductive reasoning. In the end, science involves both deductive and inductive processes.

For example, case studies are heavily weighted on the side of empirical observations. Thus, case studies are closely associated with inductive processes as researchers gather massive amounts of observations and seek interesting patterns (new ideas) in the data. Experimental research, on the other hand, puts great emphasis on deductive reasoning.

What is a Theory?

We have stated that theories and hypotheses are ideas, but what sort of ideas are they, exactly? A  theory  is a well-developed set of ideas that propose an explanation for observed phenomena. Theories are repeatedly checked against the world, but they tend to be too complex to be tested all at once; instead, researchers create hypotheses to test specific aspects of a theory.

A  hypothesis is a testable prediction about how the world will behave if our idea is correct, and it is often worded as an if-then statement (e.g., if I study all night, then I will get a passing grade on the test). The hypothesis is critical because it bridges the gap between the realm of ideas and the real world. As specific hypotheses are tested, theories are modified and refined to reflect and incorporate the result of these tests (Figure 3.3).

forming a hypothesis is accomplished through deductive reasoning

Figure 3.3 .  The scientific method of research includes proposing hypotheses, conducting research, and creating or modifying theories based on results.

To see how this process works, let us consider a specific theory and a hypothesis that might be generated from that theory. The James-Lange theory of emotion asserts that emotional experience relies on the physiological arousal associated with the emotional state. If you walked out of your home and discovered a very aggressive snake waiting on your doorstep, your heart would begin to race and your stomach churn. According to the James-Lange theory, these physiological changes would result in your feeling of fear. A hypothesis that could be derived from this theory might be that a person who does not experience physiological arousal that the sight of the snake elicits will not feel fear.

A scientific hypothesis is also  falsifiable , or capable of being shown to be incorrect. For example, Sigmund Freud had lots of interesting ideas to explain various human behaviors; however, a major criticism of Freud’s theories is that many of his ideas cannot be empirically tested and thus are not falsifiable. Despite this, Freud’s theories are widely taught in introductory psychology texts because of their historical significance for personality psychology and psychotherapy, and these remain the root of all modern forms of therapy.

In contrast, the James-Lange theory does generate falsifiable hypotheses, such as the one described above. Some individuals who suffer significant injuries to their spinal columns are unable to feel the bodily changes that often accompany emotional experiences. Therefore, we could test the hypothesis by determining how emotional experiences differ between individuals who can detect these changes in their physiological arousal and those who do not. In fact, this research has been conducted, and while the emotional experiences of people deprived of an awareness of their physiological arousal may be less intense, they still experience emotion (Chwalisz, Diener, & Gallagher, 1988).

Scientific research’s dependence on falsifiability allows for high confidence in the information that it produces. Typically, by the time information is accepted by the scientific community, it has been tested repeatedly. However, in the early years of studying development, theories were often based on philosophy and inductive reasoning, and could not always provide empirical evidence to support these ideas. In the late 1800s,  a transformation occurred when philosophy met physiology and adopted the scientific methods used by the physical sciences to explore questions about behavior and the mind.

Video 3.1 Hypothesis vs Theory explains the differences between the two.

ideas are tested against the empirical world

empirical observations lead to new ideas

capable of being shown to be incorrect

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Psychological Approaches

Understanding theories.

Scientific knowledge is advanced through a process known as the scientific method. Basically, ideas (in the form of theories and hypotheses) are tested against the real world (in the form of empirical observations), and those empirical observations lead to more ideas that are tested against the real world, and so on. In this sense, the scientific process is circular. The types of reasoning within the circle are called deductive and inductive. In  deductive reasoning , ideas are tested against the empirical world; in  inductive reasoning , empirical observations lead to new ideas (Figure 2.4). These processes are inseparable, like inhaling and exhaling, but different research approaches place different emphasis on the deductive and inductive aspects.

forming a hypothesis is accomplished through deductive reasoning

Figure  3.2.1.  Psychological research relies on both inductive and deductive reasoning.

In the scientific context, deductive reasoning begins with a generalization—one hypothesis—that is then used to reach logical conclusions about the real world. If the hypothesis is correct, then the logical conclusions reached through deductive reasoning should also be correct. A deductive reasoning argument might go something like this: All living things require energy to survive (this would be your hypothesis). Ducks are living things. Therefore, ducks require energy to survive (logical conclusion). In this example, the hypothesis is correct; therefore, the conclusion is correct as well. Sometimes, however, an incorrect hypothesis may lead to a logical but incorrect conclusion. Consider this argument: all ducks are born with the ability to see. Quackers is a duck. Therefore, Quackers was born with the ability to see. Scientists use deductive reasoning to test their hypotheses empirically. Returning to the example of the ducks, researchers might design a study to test the hypothesis that if all living things require energy to survive, then ducks will be found to require energy to survive.

Deductive reasoning starts with a generalization that is tested against real-world observations; however, inductive reasoning moves in the opposite direction. Inductive reasoning uses empirical observations to construct broad generalizations. Unlike deductive reasoning, conclusions drawn from inductive reasoning may or may not be correct, regardless of the observations on which they are based. For instance, you may notice that your favorite fruits—apples, bananas, and oranges—all grow on trees; therefore, you assume that all fruit must grow on trees. This would be an example of inductive reasoning, and the existence of strawberries, blueberries, and kiwi demonstrate that this generalization is not correct despite it being based on several direct observations. Scientists use inductive reasoning to formulate theories, which in turn generate hypotheses that are tested with deductive reasoning. In the end, science involves both deductive and inductive processes.

For example, case studies are heavily weighted on the side of empirical observations. Thus, case studies are closely associated with inductive processes as researchers gather massive amounts of observations and seek interesting patterns (new ideas) in the data. Experimental research, on the other hand, puts great emphasis on deductive reasoning.

What is a Theory?

We have stated that theories and hypotheses are ideas, but what sort of ideas are they, exactly? A  theory  is a well-developed set of ideas that propose an explanation for observed phenomena. Theories are repeatedly checked against the world, but they tend to be too complex to be tested all at once; instead, researchers create hypotheses to test specific aspects of a theory.

A  hypothesis  is a testable prediction about how the world will behave if our idea is correct, and it is often worded as an if-then statement (e.g., if I study all night, I will get a passing grade on the test). The hypothesis is critical because it bridges the gap between the realm of ideas and the real world. As specific hypotheses are tested, theories are modified and refined to reflect and incorporate the result of these tests (Figure 3.2).

forming a hypothesis is accomplished through deductive reasoning

Figure  3.2.2.  The scientific method of research includes proposing hypotheses, conducting research, and creating or modifying theories based on results.

To see how this process works, let us consider a specific theory and a hypothesis that might be generated from that theory. The James-Lange theory of emotion asserts that emotional experience relies on the physiological arousal associated with the emotional state. If you walked out of your home and discovered a very aggressive snake waiting on your doorstep, your heart would begin to race and your stomach churn. According to the James-Lange theory, these physiological changes would result in your feeling of fear. A hypothesis that could be derived from this theory might be that a person who is unaware of the physiological arousal that the sight of the snake elicits will not feel fear.

A scientific hypothesis is also  falsifiable or capable of being shown to be incorrect. For example, Sigmund Freud had lots of interesting ideas to explain various human behaviors; however, a major criticism of Freud’s theories is that many of his ideas cannot be empirically tested and thus are not falsifiable. Despite this, Freud’s theories are widely taught in introductory psychology texts because of their historical significance for personality psychology and psychotherapy, and these remain the root of all modern forms of therapy.

In contrast, the James-Lange theory does generate falsifiable hypotheses, such as the one described above. Some individuals who suffer significant injuries to their spinal columns are unable to feel the bodily changes that often accompany emotional experiences. Therefore, we could test the hypothesis by determining how emotional experiences differ between individuals who can detect these changes in their physiological arousal and those who do not. In fact, this research has been conducted, and while the emotional experiences of people deprived of an awareness of their physiological arousal may be less intense, they still experience emotion (Chwalisz, Diener, & Gallagher, 1988).

Scientific research’s dependence on falsifiability allows for high confidence in the information that it produces. Typically, by the time information is accepted by the scientific community, it has been tested repeatedly. However, in the early years of studying development, theories were often based on philosophy and inductive reasoning, and could not always provide empirical evidence to support these ideas. In the late 1800s,  a transformation occurred when philosophy met physiology and adopted the scientific methods used by the physical sciences to explore questions about behavior and the mind.

Video 3.2.1.  Hypothesis vs Theory explains the differences between the two.

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  1. What Is Deductive Reasoning

    forming a hypothesis is accomplished through deductive reasoning

  2. Research Hypothesis: Definition, Types, Examples and Quick Tips

    forming a hypothesis is accomplished through deductive reasoning

  3. 15 Deductive Reasoning Examples (2023)

    forming a hypothesis is accomplished through deductive reasoning

  4. Forming a hypothesis is accomplished through reasoning and writing

    forming a hypothesis is accomplished through deductive reasoning

  5. What Is Deductive Reasoning? Definition, Examples & How To Use It

    forming a hypothesis is accomplished through deductive reasoning

  6. How to Write a Strong Hypothesis in 6 Simple Steps

    forming a hypothesis is accomplished through deductive reasoning

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  3. 1.5. Hypothesis statement

  4. level 3 inference hypothesis

  5. Limitations of Hypothesis

  6. Forming A Hypothesis Video TNOTES

COMMENTS

  1. What Is Deductive Reasoning?

    Methodology What Is Deductive Reasoning? | Explanation & Examples What Is Deductive Reasoning? | Explanation & Examples Published on January 20, 2022 by Pritha Bhandari . Revised on June 22, 2023. Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions.

  2. Deductive Approach (Deductive Reasoning)

    In studies with deductive approach, the researcher formulates a set of hypotheses at the start of the research. Then, relevant research methods are chosen and applied to test the hypotheses to prove them right or wrong. Generally, studies using deductive approach follow the following stages: Deducing hypothesis from theory.

  3. How to Write a Strong Hypothesis

    Knowledge Base Methodology How to Write a Strong Hypothesis | Steps & Examples How to Write a Strong Hypothesis | Steps & Examples Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023. A hypothesis is a statement that can be tested by scientific research.

  4. Deductive Reasoning

    Definition: Deductive reasoning is a logical process in which a conclusion is drawn from a set of premises or propositions that are assumed or known to be true. The process of deductive reasoning starts with a general statement or premise, and then moves towards a specific conclusion that logically follows from the initial statement.

  5. 1.2: The Science of Biology

    Figure 1.2. 1: Scientific Reasoning: Scientists use two types of reasoning, inductive and deductive, to advance scientific knowledge. Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. This type of reasoning is common in descriptive science. A life scientist such as a biologist ...

  6. 2.4 Developing a Hypothesis

    Typically, we begin with a broad and general theory and use deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use inductive reasoning which involves using specific observations or research findings to form a more general hypothesis.

  7. The Research Hypothesis: Role and Construction

    There are three primary modes of inference by which hypotheses are developed: deduction (reasoning from a general propositions to specific instances), induction (reasoning from specific instances to a general proposition), and abduction (formulation/acceptance on probation of a hypothesis to explain a surprising observation).

  8. 3.5: Developing A Hypothesis

    As Figure 3.5. 1 shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of "theoretically motivated" or "theory-driven" research. Figure 3.5. 1: Hypothetico-deductive method combined with the general model of scientific research in psychology.

  9. Deductive reasoning vs. Inductive reasoning

    Deductive reasoning, also known as deduction, is a basic form of reasoning. It starts out with a general statement, or hypothesis, and examines the possibilities to reach a specific,...

  10. The potential of working hypotheses for deductive ...

    While hypotheses frame explanatory studies and provide guidance for measurement and statistical tests, deductive, exploratory research does not have a framing device like the hypothesis. To this purpose, this article examines the landscape of deductive, exploratory research and offers the working hypothesis as a flexible, useful framework that can guide and bring coherence across the steps in ...

  11. 15. OBSERVATION AND HYPOTHESIZING Flashcards

    Deductive reasoning-. A prediction made or a conclusion drawn from insight gained from an established principle or law. Hypothesis-. An initial proposed explanation for the phenomenon. Inductive reasoning-. Drawing a conclusion from many observations concerning a possible common cause and forming a general rule. Law-.

  12. 1.2 The Process of Science

    Deductive reasoning or deduction is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning. Deductive reasoning is a form of logical thinking that uses a general principle or law to predict specific results. From those general ...

  13. What Is Deductive Reasoning?

    Deductive vs inductive reasoning. Deductive reasoning is a top-down approach, while inductive reasoning is a bottom-up approach. In deductive reasoning, you start with general ideas and work toward specific conclusions through inferences. Based on theories, you form a hypothesis. Using empirical observations, you test that hypothesis using ...

  14. A hypothetico‐deductive theory of science and learning

    These three practices are (1) constructing explanations, (2) designing experiments, and (3) interpreting data. These practices all rely heavily on hypothetical thinking but differ according to how or when hypothetical thinking is initiated. In its most classic form, HD reasoning (Butts, 2015) is initiated from a hypothesis to test. Hypothetical ...

  15. Understanding Theories

    If the hypothesis is correct, then the logical conclusions reached through deductive reasoning should also be correct. A deductive reasoning argument might go something like this: All living things require energy to survive (this would be your hypothesis). Ducks are living things. Therefore, ducks require energy to survive (logical conclusion).

  16. 8.1: Hypothetical Reasoning

    Example 8.1.2 8.1. 2. After collecting some evidence, you weigh the reasons in favor of thinking that each suspect is indeed the murderer, and you decide that the spouse is responsible. 3. Drawing implications from the hypothesis. Example 8.1.3 8.1. 3. If the spouse was the murderer, then a number of things follow.

  17. Deductive reasoning

    Deductive reasoning is the mental process of drawing deductive inferences.An inference is deductively valid if its conclusion follows logically from its premises, i.e. it is impossible for the premises to be true and the conclusion to be false.. For example, the inference from the premises "all men are mortal" and "Socrates is a man" to the conclusion "Socrates is mortal" is deductively valid.

  18. Deductive Reasoning (Definition + Examples)

    Deductive reasoning is introduced in math classes to help students understand equations and create proofs. When math teachers discuss deductive reasoning, they usually talk about syllogisms. Syllogisms are a form of deductive reasoning that help people discover a truth. Here's an example. The sum of any triangle's three angles is 180 degrees.

  19. Understanding Theories

    If the hypothesis is correct, then the logical conclusions reached through deductive reasoning should also be correct. A deductive reasoning argument might go something like this: All living things require energy to survive (this would be your hypothesis). Ducks are living things. Therefore, ducks require energy to survive (logical conclusion).

  20. Forming a hypothesis is accomplished through deductive reasoning

    Answer Therefore, the correct answer is inductive reasoning. Inductive reasoning involves observing patterns or data and using that information to form a hypothesis or generalization. This is the most common method for forming a hypothesis in scientific research. Video Answer Solved by verified expert Oops! There was an issue loading this video

  21. Understanding Theories

    If the hypothesis is correct, then the logical conclusions reached through deductive reasoning should also be correct. A deductive reasoning argument might go something like this: All living things require energy to survive (this would be your hypothesis). Ducks are living things. Therefore, ducks require energy to survive (logical conclusion).

  22. Forming a hypothesis is accomplished through _______ reasoning. A

    Forming a hypothesis is accomplished through inductive reasoning.Inductive reasoning is a type of reasoning that involves making conclusions or generalizations based on specific observations or evidence.