sample research proposal for hypothetico deductive method

HYPOTHETICO-DEDUCTIVE METHOD: A COMPARATIVE ANALYSIS

• International Journal of Sciences Basic and Applied Research (IJSBAR) 7(4):2395-3446

• Abu Dhabi University

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hypothetico-deductive method

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hypothetico-deductive method , procedure for the construction of a scientific theory that will account for results obtained through direct observation and experimentation and that will, through inference , predict further effects that can then be verified or disproved by empirical evidence derived from other experiments.

An early version of the hypothetico-deductive method was proposed by the Dutch physicist Christiaan Huygens (1629–95). The method generally assumes that properly formed theories are conjectures intended to explain a set of observable data. These hypotheses , however, cannot be conclusively established until the consequences that logically follow from them are verified through additional observations and experiments. The method treats theory as a deductive system in which particular empirical phenomena are explained by relating them back to general principles and definitions. However, it rejects the claim of Cartesian mechanics that those principles and definitions are self-evident and valid; it assumes that their validity is determined only by the exact light their consequences throw on previously unexplained phenomena or on actual scientific problems.

THE HYPOTHETICO-DEDUCTIVE METHOD

• It Is Been Proposed An Austrian Philosopher , Karl Popper.
• It Is A Typical Version Of Scientific Method.
• It Has Seven Steps.

Identify a broad problem area

Define the problem statement

Develop hypotheses

• Determine measures

Data collection

• Data analysis
• Interpretation of data
• should be hand written
• Deadline – Next Class
• Should be in a presentable manner

What are the steps in hypothetico-deductive research with reference to new version of book? Is it really different to old version? If so identify with your own point of view?

The hypothetico-Deductive Method

The Seven steps involved in the hypothetico deductive method of research

from the building blocks are listed below:

If the manager notice a drop in sales , incorrect accounting results , low-yielding investment , disinterestedness of employees in their work, and the like, could attract the attention of the manager to do a research project.

• Scientific research starts with a definite aim or purpose.
• A problem statement states the general objective of the research.
• The network of associations between the problem and the variables that affect it is identified.
• A scientific hypothesis must meet two requirements:
• The hypothesis must be testable
• The hypothesis must be falsifiable (we can only prove our hypotheses until they are disproved).

Determine Measures

• The variables in the theoretical framework should be measurable in some way.
• Some variables cannot be measure quantitatively , such as unresponsive employees , we need to operationalize this variable.
• Measurement of variables is discussed in Ch. 6 & 7.
• Data with respect to each variable in the hypothesis need to be obtained.

There are two types of data:

-Quantitative data

-Qualitative data

• Data Analysis
• In this step, the data gathered are statistically analyzed to see if the hypotheses that were generated have been supported.
• Analyses of both quantitative and qualitative data can be done to determine if certain relations are important.
• Qualitative data refer to information gathered through interviews and observations. These data usually for objects than can not be physically measured, like feelings and attitudes.
• Quantitative data refer to information gathered about objects that can be physically measured. The researcher could obtain these data through the company records, government statistics, or any formal records.
• Now we must decide whether our hypotheses are supported or not by interpreting the meaning of the results or the data analysis.
• Based on these results, the researcher would make recommendations in order to solve the problem in hand.

Example 2.2 of the Application of the Hypothetico-Deductive Method

• Observation of the CIO Dilemma

The Chief Information Officer (CIO) of a firm observes that the newly installed Management Information System (MIS) is not being used by middle managers as much as was originally expected.

“There is surely a problem here,” the CIO exclaims.

Example 2.2 (cont.)

• Information Gathering through Informal Interviews

- Talking to some of the middle-level managers, the CIO finds that many of them have very little idea as to what MIS is all about, what kinds of information it could provide, and how to access it and utilize the information.

• Obtaining More Information through Literature Survey

- The CIO immediately uses the Internet to explore further information on the lack of use of MIS in organizations.

- The search indicates that many middle-level managers are not familiar with operating personal computers.

- Lack of knowledge about what MIS offers is also found to be another main reason why some managers do not use it.

• Formulating a Theory

- based on all this information, the CIO develops a theory incorporating all the relevant factors contributing to the lack of access to the MIS by managers in the organization.

• Hypothesizing

From such a theory, the CIO generates various hypotheses for testing, one among them being:

- Knowledge of the usefulness of MIS would help managers to put it to greater use.

• Data Collection

The CIO then develops a short questionnaire on the various factors theorized to influence the use of the MIS by managers , such as:

• The extent of knowledge of what MIS is
• What kinds of information MIS provides
• How to gain access to the information
• The level of comfort felt by managers in using computers in general
• How often managers have used the MIS in the preceding 3 months.

The CIO then analyzes the data obtained through the questionnaire to see what factors prevent the managers from using the system.

Based on the results, the manager deduces or concludes that managers do not use MIS owing to certain factors.

• These deductions help the CIO to take necessary actions to solve the problem, which might include, among other things:

- Organizing seminars for training managers on the use of computers, and

- MIS and its usefulness.

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• > Theories of Scientific Method
• > The hypothetico-deductive method

Book contents

• Frontmatter
• Abbreviations
• Acknowledgements
• Introduction
• I The idea of methodology
• II Inductive and hypothetico-deductive methods
• 5 Induction in science
• 6 Some justifications of induction
• 7 The hypothetico-deductive method
• III Probability and scientific method
• IV Popper and his rivals
• V Naturalism, pragmatism, realism and methodology
• Bibliography

7 - The hypothetico-deductive method

from II - Inductive and hypothetico-deductive methods

As the name indicates there are at least two parts to the hypothetico-deductive (h-d) method: a hypothetico part in which a hypothesis or theory, arising from whatever source, is proposed for test, and a deductive part in which test consequences are drawn from the hypotheses. Unmentioned in the name of the method is a crucial third part in which consequences are deduced and compared with experiment or what we can observe. The consequences pass or fail when the comparison is made. In some cases the hypothesis might be invented to account for some already known fact(s); it is then tested by deducing further consequences from it, which are then subject to test. An important question arises about how the pass or fail verdict is transmitted back to the hypothesis; this creates problems for the h-d method, as will be seen. The test consequences need not be obtained only by deduction; if the hypotheses are statistical then the consequences are inferred by non-deductive, or inductive, reasoning. So a better name might be the hypothetico-inferential method , to cover both cases of deductive and non-deductive inference.

The method has had a long history from the time of Plato when it went by other names in his dialogues such as “the method of hypothesis”. It was applied to science in medieval times and since then has had a long involvement with scientific method. It became central in the nineteenth-century debate over method between Whewell and J. S. Mill. Some say its day has now come and its involvement in methodology is largely over. The task of this chapter is to spell out the nature of this method, and its strengths and weaknesses.

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• The hypothetico-deductive method
• Robert Nola , University of Auckland
• Book: Theories of Scientific Method
• Online publication: 05 February 2013
• Chapter DOI: https://doi.org/10.1017/UPO9781844653881.008

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Muhammad Ibrahim Soomro

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Mastering the Hypothetico-Deductive Method: 7 Steps to Effective Business Research

I. introduction.

A hypothetico-deductive method is a valuable approach in business research that can help organizations make informed decisions based on empirical data. By following a set of structured steps, business researchers can test hypotheses and arrive at meaningful conclusions that can inform strategic planning, marketing campaigns, and other important business decisions.

• Explanation of the hypothetico-deductive method and its importance in scientific research

One of the key benefits of the hypothetico-deductive method in business research is that it provides a systematic approach to hypothesis testing. By identifying specific variables and testing their impact on a given outcome, business researchers can gain a deeper understanding of the mechanisms at work in a given business process or market trend. This approach can help organizations identify areas where they can improve efficiency, reduce costs, and increase revenue.

Another important aspect of the hypothetico-deductive method in business research is that it encourages researchers to develop testable hypotheses. By focusing on specific variables and testing their impact on a given outcome, business researchers can more effectively identify causal relationships and gain a deeper understanding of the factors that drive success in their industry. This can help organizations stay ahead of the competition and identify new opportunities for growth.

Finally, the hypothetico-deductive method emphasizes the importance of empirical data in business research. By collecting and analyzing data in a rigorous and systematic way, business researchers can arrive at conclusions that are based on evidence, rather than conjecture or personal opinion. This can help organizations make more informed decisions and avoid costly mistakes.

In conclusion, the hypothetico-deductive method is a valuable tool for business research that provides a structured approach to hypothesis testing, encourages the development of testable hypotheses, and emphasizes the importance of empirical data. By following this method, business researchers can more effectively gather and analyze data, and draw valid conclusions that can inform strategic planning, marketing campaigns, and other important business decisions.

II. Seven Steps of the Hypothetico-Deductive Method

When conducting business research using the hypothetico-deductive method, there are seven key steps that researchers should follow. These steps include identifying a broad problem area, defining the problem statement, developing hypotheses, determining measures of data collection, collecting data, analyzing data, and interpreting data.

• A. Identify a broad problem area

The first step of the hypothetico-deductive method is to identify a broad problem area that could trigger a research project. A problem area is a general issue or topic that needs to be addressed. Identifying a problem area is important because it helps to focus the research and provide direction.

Examples of problem areas that could trigger a research project

There are various problem areas that could trigger a research project, depending on the industry, organization, or context. Here are a few examples:

• Sales: A decrease in sales could be a problem area that triggers a research project. The manager could investigate the reasons behind the decline in sales and develop strategies to increase sales.
• Employee engagement: If the employees in an organization seem disinterested in their work, it could lead to a decrease in productivity and efficiency. A research project could be conducted to identify the reasons behind the disinterest and develop strategies to increase employee engagement.
• Customer satisfaction: Customer satisfaction is crucial for the success of any business. If customers are not satisfied with the products or services, it could lead to a decrease in sales and reputation. A research project could be conducted to identify the areas of improvement and develop strategies to increase customer satisfaction.

Other problem areas that could trigger a research project include incorrect accounting results, low-yielding investments, a high employee turnover rate, ineffective marketing strategies, and more.

It is important to note that the problem area should be broad enough to allow for multiple hypotheses to be developed, but specific enough to provide focus to the research project. Once the problem area has been identified, the next step is to define the problem statement, which will be discussed in the next section.

• B. Define the problem statement

The second step of the hypothetico-deductive method is to define the problem statement. A problem statement is a clear and concise statement that defines the problem to be addressed in the research project. The problem statement sets the direction and focus for the research, and provides a basis for the development of hypotheses.

Importance of having a clear problem statement

It is important to have a clear problem statement because it ensures that the research is focused on a specific problem, rather than a vague or general issue. A clear problem statement helps to ensure that the research is relevant and meaningful and that the results will provide insights and solutions to the problem at hand.

Here are a few examples of clear problem statements:

• “The purpose of this research is to investigate the reasons behind the decline in sales and develop strategies to increase sales.”
• “The objective of this research is to identify the factors that contribute to employee disinterest and develop strategies to increase employee engagement.”
• “The goal of this research is to identify the areas of improvement in customer service and develop strategies to increase customer satisfaction.”

Each of these problem statements clearly defines the problem to be addressed in the research project and provides direction for the development of hypotheses and the collection of data.

In order to develop a clear problem statement, it is important to have a thorough understanding of the problem area and the context in which it exists. It is also important to ensure that the problem statement is specific, concise, and measurable, so that progress can be tracked throughout the research project.

Once the problem statement has been defined, the next step is to develop hypotheses, which will be discussed in the next section.

• C. Develop hypotheses

The third step in the hypothetico-deductive method is to develop hypotheses. Hypotheses are educated guesses or tentative explanations for the problem identified in the problem statement. They are used to guide the research and provide a basis for the collection and analysis of data.

Explanation of how to identify variables and develop hypotheses

To develop hypotheses, it is necessary to identify the variables that are relevant to the problem at hand. Variables are factors that can affect the outcome of the research project. They can be divided into independent variables and dependent variables.

Independent variables are variables that are manipulated or controlled by the researcher. They are the variables that are believed to have an effect on the dependent variable. For example, in a study on the effects of exercise on weight loss, the independent variable would be exercise.

Dependent variables, on the other hand, are variables that are influenced by the independent variables. They are the variables that the researcher is trying to explain or predict. In the above example, the dependent variable would be weight loss.

Once the variables have been identified, hypotheses can be developed. Hypotheses should be testable and based on prior knowledge or research. They should be specific and clearly state the relationship between the independent and dependent variables.

Here are a few examples of hypotheses:

• “Increasing the amount of exercise will lead to a greater amount of weight loss.”
• “Increasing employee engagement will lead to a decrease in turnover rates.”
• “Providing better customer service will lead to an increase in customer satisfaction.”

In each of these examples, the hypothesis clearly states the relationship between the independent and dependent variables. This allows for the collection of data that can be used to test the hypothesis.

In summary, developing hypotheses is an important step in the hypothetico-deductive method of research. Hypotheses provide a clear direction for the research and allow for the collection of data that can be used to test the relationship between variables. By identifying the relevant variables and developing hypotheses, researchers can begin to collect data that will lead to a better understanding of the problem at hand.

• D. Determine measures of data collection

In the hypothetico-deductive method, after identifying the variables, the next step is to determine how to measure them. This involves operationalizing the variables, which means defining them in terms of observable and measurable characteristics. For example, if the variable of interest is employee satisfaction, the researcher must operationalize it by defining it in terms of specific questions that can be asked in a survey.

Explanation of how to operationalize variables and collect data

There are two types of variables: dependent and independent. Dependent variables are the outcomes that the researcher is interested in studying, while independent variables are the factors that may influence the dependent variable. For example, in a study on employee satisfaction, the dependent variable is the level of satisfaction, while the independent variables may include factors such as salary, job security, and work-life balance.

To determine measures of data collection, researchers must first operationalize the independent and dependent variables. This can be done through surveys, questionnaires, interviews, observations, or other data collection methods. The data collection method chosen will depend on the nature of the research question and the variables being studied.

For example, if the research question is focused on employee satisfaction, a survey may be the most appropriate data collection method. The survey questions should be designed to operationalize the variables of interest, such as asking about specific aspects of the job that may affect satisfaction. The researcher may also choose to use a Likert scale to measure the level of satisfaction, with responses ranging from strongly disagree to strongly agree.

It is important to ensure that the data collection method is reliable and valid. Reliability refers to the consistency of the data collection method, while validity refers to whether the data collected is measuring what it is intended to measure. To ensure reliability, the researcher should test the data collection method on a sample of the population and assess its consistency. To ensure validity, the researcher should review the operationalization of the variables and the data collection method to ensure that it is measuring what it is intended to measure.

In summary, determining measures of data collection involves operationalizing the variables of interest and choosing an appropriate data collection method. The researcher should ensure that the method is reliable and valid to ensure accurate results.

• E. Data collection

Data collection is a critical step in the hypothetico-deductive method of research. It involves obtaining data with respect to each variable in the hypothesis. There are two main types of data that can be collected: quantitative and qualitative

Explanation of the two types of data: quantitative and qualitative

Quantitative data refers to information that can be expressed numerically and can be statistically analyzed. This type of data is gathered through structured methods such as surveys, experiments, and observations. Quantitative data is useful for identifying patterns, relationships, and trends in large datasets. Examples of quantitative data include sales figures, test scores, and demographic information.

Qualitative data, on the other hand, refers to non-numerical information that can provide insights into attitudes, beliefs, and behaviors. This type of data is collected through methods such as interviews, focus groups, and observations. Qualitative data can provide a deeper understanding of complex issues and can be used to generate hypotheses. Examples of qualitative data include customer feedback, employee opinions, and observations of workplace dynamics.

Both types of data have their strengths and limitations, and researchers often use a combination of both to gain a more comprehensive understanding of the problem area. It is important to select appropriate data collection methods and techniques that are aligned with the research objectives and hypotheses. Proper data collection ensures that the data collected is reliable, valid, and relevant for the research project.

• F. Data analysis

Data analysis is an essential step in the hypothetico-deductive method of research. This step involves analyzing the data collected during the previous step to determine if the hypotheses generated have been supported or not.

Explanation of how to analyze both quantitative and qualitative data

There are two types of data that can be collected: quantitative and qualitative. Quantitative data are numerical and can be measured, while qualitative data are non-numerical and are based on observations, interviews, or other forms of subjective information.

Quantitative data analysis involves using statistical tools and techniques to analyze numerical data. The most common techniques used in quantitative data analysis include regression analysis, correlation analysis, and hypothesis testing. These techniques are used to identify patterns, relationships, and trends in the data.

Qualitative data analysis, on the other hand, involves identifying themes, patterns, and categories in the data. This type of analysis is usually conducted using a variety of methods, such as content analysis, narrative analysis, and grounded theory. Qualitative data analysis is more subjective than quantitative data analysis, and the results are more open to interpretation.

In both types of data analysis, it is important to remain objective and avoid any biases that could influence the results. It is also important to ensure that the data analysis is conducted in a systematic and rigorous manner, to ensure that the results are valid and reliable.

Overall, data analysis is a crucial step in the hypothetico-deductive method of research, as it helps to determine if the hypotheses generated have been supported by the data. It is important to use appropriate tools and techniques for both quantitative and qualitative data analysis and to remain objective throughout the process.

• G. Interpretation of data

Once the data has been collected and analyzed, the next step is to interpret the data to draw conclusions. The researcher must decide whether their hypotheses are supported by the data or not. This involves making sense of the results and understanding their implications for the research question.

Explanation of how to interpret data and draw conclusions

In quantitative research, statistical methods are often used to analyze the data, and the results are reported using numbers and graphs. The researcher may look for patterns or relationships between variables and use statistical tests to determine the significance of those relationships.

In qualitative research, the data may be analyzed through a process of coding, where themes and patterns are identified in the data. The researcher may use techniques such as content analysis or grounded theory to help them make sense of the data.

Once the data has been analyzed and interpreted, the researcher can draw conclusions about the research question. They may be able to confirm or reject their hypotheses, or they may find that the data raises new questions or suggests new avenues for further research.

The interpretation of data is a crucial step in the research process, as it allows the researcher to make informed decisions and draw meaningful conclusions. It is important to approach data interpretation objectively, using sound methods and techniques, to ensure that the conclusions drawn are valid and reliable.

III. Conclusion

In conclusion, a hypothetico-deductive method is a valuable tool for conducting scientific research. It allows researchers to systematically identify and address problems, develop hypotheses, collect and analyze data, and draw conclusions based on the evidence gathered. By following the seven steps of this method, researchers can ensure that their research is rigorous and objective and that their findings are reliable and valid.

• Importance of the hypothetico-deductive method in scientific research

The importance of having a clear problem statement, developing testable hypotheses, and operationalizing variables cannot be overstated. These steps are critical to ensuring that the research is focused and specific, and that the data collected is relevant to the research question. Similarly, the proper collection and analysis of both quantitative and qualitative data is essential for drawing meaningful conclusions and making evidence-based recommendations.

• Potential applications of the method in different fields and industries

The hypothetico-deductive method is widely applicable in different fields and industries, including business, healthcare, education, and social sciences. By using this method, researchers can gain insights into complex problems, identify opportunities for improvement, and develop evidence-based solutions to real-world challenges.

In conclusion, the hypothetico-deductive method is a powerful and effective approach to conducting scientific research. By following the seven steps outlined above, researchers can generate new knowledge, test hypotheses, and make informed decisions based on the evidence gathered. With its emphasis on objectivity, rigor, and systematic inquiry, the hypothetico-deductive method remains a cornerstone of scientific inquiry and a valuable tool for advancing knowledge in all fields.

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Overview of the Scientific Method

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 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) [1] . 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 observations 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) [2] . 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). Researchers begin with a set of phenomena and either construct a theory to explain or interpret them 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 prediction 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 2.3  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.

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) [3] . 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] [4] 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 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.

• Zajonc, R. B. (1965). Social facilitation.  Science, 149 , 269–274 ↵
• 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. ↵

A coherent explanation or interpretation of one or more phenomena.

A specific prediction about a new phenomenon that should be observed if a particular theory is accurate.

A cyclical process of theory development, starting with an observed phenomenon, then developing or using a theory to make a specific prediction of what should happen if that theory is correct, testing that prediction, refining the theory in light of the findings, and using that refined theory to develop new hypotheses, and so on.

The ability to test the hypothesis using the methods of science and the possibility to gather evidence that will disconfirm the hypothesis if it is indeed false.

Research Methods in Psychology Copyright © 2019 by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Hypothetico-Deductive Method

The hypothetico-deductive method is one of the mainstays of scientific research, often regarded as the only 'true' scientific research method.

This article is a part of the guide:

• Falsifiability
• Inductive Reasoning
• Deductive Reasoning
• Scientific Reasoning
• Testability

Browse Full Outline

• 1 Scientific Reasoning
• 2.1 Falsifiability
• 2.2 Verification Error
• 2.3 Testability
• 2.4 Post Hoc Reasoning
• 3 Deductive Reasoning
• 4.1 Raven Paradox
• 5 Causal Reasoning
• 6 Abductive Reasoning
• 7 Defeasible Reasoning

This area fuels intense debate and discussion between many fields of scientific specialization.

Concisely, the method involves the traditional steps of observing the subject, in order to elaborate upon an area of study. This allows the researcher to generate a testable and realistic hypothesis .

The hypothesis must be falsifiable by recognized scientific methods but can never be fully confirmed, because refined research methods may disprove it at a later date.

From the hypothesis , the researcher must generate some initial predictions, which can be proved, or disproved, by the experimental process . These predictions must be inherently testable for the hypothetico-deductive method to be a valid process.

For example, trying to test the hypothesis that God exists would be difficult, because there is no scientific way to evaluate it.

Generating and Analyzing the Data

The next stage is to perform the experiment , obtaining statistically testable results , which can be used to analyze the results and determine whether the hypothesis has validity or has little foundation. This experiment must involve some manipulation of variables to allow the generation of analyzable data.

Finally, statistical tests will confirm whether the predictions are correct or not. This method is usually so rigorous that it is rare for a hypothesis to be completely proved, but some of the initial predictions may be correct and will lead to new areas of research and refinements of the hypothesis.

Assessing the Validity of the Hypothesis

Proving and confirming a hypothesis is never a clear-cut and definitive process. Statistics is a science based on probability, and however strong the results generated; there is always a chance of experimental error .

In addition, there may be another unknown reason that explains the results. Most theories, however solid the proof, develop and evolve over time, changing and adapting as new research refines the known data.

Proving a hypothesis is never completely accurate but, after a process of debate and retesting of the results, may become a scientific assumption. Science is built upon these ' paradigms ' and even commonly accepted views may prove to be inaccurate upon further exploration.

A false hypothesis does not necessarily mean that the area of research is now closed or incorrect. The experiment may not have been accurate enough, or there may have been some other contributing error.

This is why the hypothetico-deductive method relies on initial predictions; very few hypotheses, if the research is thorough, are completely wrong as they generate new directions for future research.

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Martyn Shuttleworth (Oct 10, 2008). Hypothetico-Deductive Method. Retrieved Sep 26, 2024 from Explorable.com: https://explorable.com/hypothetico-deductive-method

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Introduction, section snippets, references (77), cited by (26).

Information and Organization

Why is the hypothetico-deductive (h-d) method in information systems not an h-d method.

• • The hypothetico-deductive (H-D) method is reported to be common in information system (IS) literature.
• • The standard account of the H-D approach, or the Popperian H-D approach or the method of hypothesis is importantly different in the philosophy of science than the literature suggests
• • The H-D method is also often confused with the deductive-nomological (D-N) model and statistical hypotheses testing.
• • We outline method of hypothesis

The hypothetico-deductive approach in IS and the philosophy of science

The method of hypothesis, conclusions, credit authorship contribution statement, acknowledgements, realizing information systems: critical realism as an underpinning philosophy for information systems, why things happen – developing the critical realist view of causal mechanisms, serendipity in cancer drug discovery: rational or coincidence, trends in pharmacological sciences, overcoming theory-practice inconsistencies: critical realism and information systems research, inference to scientific law, discovery and rule-books, west meets east: new concepts and theories, academy of management journal, what design science is not, european journal of information systems, patters of medical discovery, logic in philosophy, science, art and drug discovery: a personal perspective, clinical science, structure of scientific theories, forensic fingerprinting discovery was a happy accident, admits “grandfather of dna”, theory change in science: strategies from mendelian genetics, laws and explanation in history, an interview by george sylvester viereck, is north american is research different from european is research, data base for advances in information systems, against method, the character of physical law, on the antibacterial action of cultures of a penicillium, with special reference to their use in the isolation of b. influenzae, clinical infectious diseases, philosophy of science naturalized, philosophy of science, explaining science: a cognitive approach, the nature of theory in information systems, mis quarterly, the philosopher’s corner: the value of feyerabend’s anarchic thinking for information systems research, the data base for advances in information systems, new state of play in information systems research: the push to the edges, the logic of invention, the logic of discovery, the journal of philosophy, the effect of serendipity in drug discovery and development, chemistry in new zealand, the process of information systems theorizing as a discursive practice, journal of information technology, editorial: the history and philosophy department, communications of the association for information systems, causal mechanisms in the social sciences, annual review of sociology, the function of general laws in history, journal of philosophy, aspects of scientific explanation and other essays in the philosophy of science, philosophy of natural science, studies in the logic of explanation, salmon on explanatory relevance, individual-specific “fingerprints” of human dna, mis research: reference disciplines and a cumulative tradition, consumption behavior of eco-friendly products and applications of ict innovation.

To enhance the practical contributions, such as helping the government to formulate the environmental policies, guiding the manufacturing enterprises to produce eco-friendly products, we considered a further empirical and quantitative analysis. Such uses of further methods and analysis are common in hypothetical-inductive methods (Siponen et al., 2020). Hence, this paper attempts to address the following issues based on a mathematical model and experimental data:

A Task-Technology-Identity Fit Model of Smartwatch Utilisation and User Satisfaction: A Hybrid SEM-Neural Network Approach

Inductive empiricism, theory specialization and scientific idealization in is theory building, common misunderstandings of deterrence theory in information systems research and future research directions, process theory: background, opportunity, and challenges, guest editorial: theories of digital transformation: a progress report.

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Hypothetico-deductive Method

Meaning of Hypothetico-deductive Method

Hypothetico-deductive method also called H-D method   is an approach to research or a method of construction of a scientific theory that will account for results procured through direct observation and experimentation and that will, through inference, forecast additional effects that can then be verified or disproved by empirical verification derived from other experiments.

An early or premature version of the hypothetico-deductive method was forwarded by the Dutch physicist Christiaan Huygens (1629–95). The method generally assumes that properly shaped theories are postulations and they are proposed to explain a set of observable data. According to him, such hypotheses cannot be decisively recognized and established until the consequences that rationally derived from them are confirmed and verified through further observations and experiments.

The hypothetico-deductive method is based on the idea of falsification, and it is an alternative to inductivism. The method looks theory as a deductive system in which particular empirical phenomena are explained by relating them to general principles and definitions.

The method treats theory as a deductive system in which particular empirical phenomena are explained by connecting them back to general principles and definitions. It assumes that the validity of theories is measured only by the exact light of their consequences or findings throw on previously unexplained phenomena or actual scientific problems.

Philosopher Karl Popper said that it is not possible to confirm a scientific theory correct by the way of induction because no evidence ensures that opposing facts or evidence will not be found. Instead, Karl Popper predicted that proper science or a scientific theory is accomplished by deduction. According to him, a scientific theory is based on deduction and it involves the process of falsification. Falsification is a particular specialized feature or attribute of hypothesis testing. It includes stating some output from theory and then finding opposing cases using experiments or observations. This methodology proposed by Popper is commonly known as the hypothetico-deductive method.

According to this method, the scientific inquisition proceeds by formulating a hypothesis and then falsified by vigorous tests on visible empirical data. Thus the hypothetico-deductive is one of the keystone foundations of scientific research and is considered as the only true scientific research method.

The hypothetico-deductive method unlike the deductive method provides more stress on the denial of the incorrect or unverifiable hypothesis and to set effort into forming the revised hypothesis, which is testable. Therefore, the hypothetico-deductive method proposes the ever-improving cycle of hypothesis formation that follows the unbroken cycle of refutation and revisions of the hypothesis unless the hypothesis is verified.

The hypothetico-deductive method confirms a theory when the prediction and observation gap is small and disconfirms when the gap is large. Most of the focuses on scientific methodology are to reduce the gap between predictions and observations.

Steps in Hypothetico-deductive Method

The hypothetico-deductive method begins with the postulation of the hypothesis that relies on general phenomena. From the stated hypothesis, some initial predictions are generated and these can be proved or disproved by the experimental process. These predictions must be intrinsically testable for the hypothetico-deductive methods to be a valid process.

Thus, the stated hypothesis must be testable and pragmatic. The proposed hypothesis is tested through analysis of the collected data from observation and other several data collecting techniques. If the hypothesis is refuted, then a revised hypothesis is formed and the same previous process is continued. If the hypothesis is confirmed, then the theory is confirmed. So, this method is said to be a scientific method of research. The following chart shows the process of hypothetico-deductive method of study.

The above chart shows the process of hypothetico-deductive method of scientific study. Somehow the process is parallel to the deductive study of scientific study. But the very significant break between these two methods occurs at the state of refusal of stated hypothesis. The deductive method is not in favor of suggesting the revision of the stated hypothesis and then re-conducting the hypothesis testing process. However, this method advocates for the revision of the hypothesis and re-operation of the hypothesis testing unless the hypothesis is verified and accepted.

The hypothetico-deductive method is the true scientific research method in which testable hypotheses can be set up and then they are justified with empirical data and observation. Falsification is the key context of this method. It involves a specific statement and then finding contrary evidence through experiments on observations. Under this model, a hypothesis is set to generate and predict some identical preconditions, the experiment is conducted and consequences are derived which are tested against the hypothesis. If the hypothesis is proved true, it becomes confirmed theory and if it is falsified we can again go for re-conduction of the observation and experimentation. 

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