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Using the Scientific Method to Solve Problems

How the scientific method and reasoning can help simplify processes and solve problems.

By the Mind Tools Content Team

The processes of problem-solving and decision-making can be complicated and drawn out. In this article we look at how the scientific method, along with deductive and inductive reasoning can help simplify these processes.

uses scientific methodology in managerial decision making and problem solving

‘It is a capital mistake to theorize before one has information. Insensibly one begins to twist facts to suit our theories, instead of theories to suit facts.’ Sherlock Holmes

The Scientific Method

The scientific method is a process used to explore observations and answer questions. Originally used by scientists looking to prove new theories, its use has spread into many other areas, including that of problem-solving and decision-making.

The scientific method is designed to eliminate the influences of bias, prejudice and personal beliefs when testing a hypothesis or theory. It has developed alongside science itself, with origins going back to the 13th century. The scientific method is generally described as a series of steps.

observations/theory
explanation/conclusion

The first step is to develop a theory about the particular area of interest. A theory, in the context of logic or problem-solving, is a conjecture or speculation about something that is not necessarily fact, often based on a series of observations.

Once a theory has been devised, it can be questioned and refined into more specific hypotheses that can be tested. The hypotheses are potential explanations for the theory.

The testing, and subsequent analysis, of these hypotheses will eventually lead to a conclus ion which can prove or disprove the original theory.

Applying the Scientific Method to Problem-Solving

How can the scientific method be used to solve a problem, such as the color printer is not working?

1. Use observations to develop a theory.

In order to solve the problem, it must first be clear what the problem is. Observations made about the problem should be used to develop a theory. In this particular problem the theory might be that the color printer has run out of ink. This theory is developed as the result of observing the increasingly faded output from the printer.

2. Form a hypothesis.

Note down all the possible reasons for the problem. In this situation they might include:

The printer is set up as the default printer for all 40 people in the department and so is used more frequently than necessary.
There has been increased usage of the printer due to non-work related printing.
In an attempt to reduce costs, poor quality ink cartridges with limited amounts of ink in them have been purchased.
The printer is faulty.

All these possible reasons are hypotheses.

3. Test the hypothesis.

Once as many hypotheses (or reasons) as possible have been thought of, then each one can be tested to discern if it is the cause of the problem. An appropriate test needs to be devised for each hypothesis. For example, it is fairly quick to ask everyone to check the default settings of the printer on each PC, or to check if the cartridge supplier has changed.

4. Analyze the test results.

Once all the hypotheses have been tested, the results can be analyzed. The type and depth of analysis will be dependant on each individual problem, and the tests appropriate to it. In many cases the analysis will be a very quick thought process. In others, where considerable information has been collated, a more structured approach, such as the use of graphs, tables or spreadsheets, may be required.

5. Draw a conclusion.

Based on the results of the tests, a conclusion can then be drawn about exactly what is causing the problem. The appropriate remedial action can then be taken, such as asking everyone to amend their default print settings, or changing the cartridge supplier.

Inductive and Deductive Reasoning

The scientific method involves the use of two basic types of reasoning, inductive and deductive.

Inductive reasoning makes a conclusion based on a set of empirical results. Empirical results are the product of the collection of evidence from observations. For example:

‘Every time it rains the pavement gets wet, therefore rain must be water’.

There has been no scientific determination in the hypothesis that rain is water, it is purely based on observation. The formation of a hypothesis in this manner is sometimes referred to as an educated guess. An educated guess, whilst not based on hard facts, must still be plausible, and consistent with what we already know, in order to present a reasonable argument.

Deductive reasoning can be thought of most simply in terms of ‘If A and B, then C’. For example:

if the window is above the desk, and
the desk is above the floor, then
the window must be above the floor

It works by building on a series of conclusions, which results in one final answer.

Social Sciences and the Scientific Method

The scientific method can be used to address any situation or problem where a theory can be developed. Although more often associated with natural sciences, it can also be used to develop theories in social sciences (such as psychology, sociology and linguistics), using both quantitative and qualitative methods.

Quantitative information is information that can be measured, and tends to focus on numbers and frequencies. Typically quantitative information might be gathered by experiments, questionnaires or psychometric tests. Qualitative information, on the other hand, is based on information describing meaning, such as human behavior, and the reasons behind it. Qualitative information is gathered by way of interviews and case studies, which are possibly not as statistically accurate as quantitative methods, but provide a more in-depth and rich description.

The resultant information can then be used to prove, or disprove, a hypothesis. Using a mix of quantitative and qualitative information is more likely to produce a rounded result based on the factual, quantitative information enriched and backed up by actual experience and qualitative information.

In terms of problem-solving or decision-making, for example, the qualitative information is that gained by looking at the ‘how’ and ‘why’ , whereas quantitative information would come from the ‘where’, ‘what’ and ‘when’.

It may seem easy to come up with a brilliant idea, or to suspect what the cause of a problem may be. However things can get more complicated when the idea needs to be evaluated, or when there may be more than one potential cause of a problem. In these situations, the use of the scientific method, and its associated reasoning, can help the user come to a decision, or reach a solution, secure in the knowledge that all options have been considered.

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The Management Science Approach to Problem Solving

For the example model developed in the previous section,

the solution technique is simple algebra. Solving the constraint equation for x , we have

Substituting the value of 25 for x into the profit function results in the total profit:

Thus, if the manager decides to produce 25 units of the product and all 25 units sell, the business firm will receive $375 in profit. Note, however, that the value of the decision variable does not constitute an actual decision; rather, it is information that serves as a recommendation or guideline, helping the manager make a decision.

Some management science techniques do not generate an answer or a recommended decision. Instead, they provide descriptive results : results that describe the system being modeled . For example, suppose the business firm in our example desires to know the average number of units sold each month during a year. The monthly data (i.e., sales) for the past year are as follows:

A management science solution can be either a recommended decision or information that helps a manager make a decision .

Monthly sales average 40 units (480 · 12). This result is not a decision; it is information that describes what is happening in the system. The results of the management science techniques in this text are examples of the two types shown in this section: (1) solutions/decisions and (2) descriptive results.

Implementation

The final step in the management science process for problem solving described in Figure 1.1 is implementation. Implementation is the actual use of the model once it has been developed or the solution to the problem the model was developed to solve. This is a critical but often overlooked step in the process. It is not always a given that once a model is developed or a solution found, it is automatically used. Frequently the person responsible for putting the model or solution to use is not the same person who developed the model and, thus, the user may not fully understand how the model works or exactly what it is supposed to do. Individuals are also sometimes hesitant to change the normal way they do things or to try new things. In this situation the model and solution may get pushed to the side or ignored altogether if they are not carefully explained and their benefit fully demonstrated. If the management science model and solution are not implemented, then the effort and resources used in their development have been wasted .

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A | Scientific Method in Organizational Research

Students of management often complain about “theoretical” or “abstract” approaches to a subject; they argue instead in favor of “relevant” and “applied” approaches. The feeling is that there usually exist two distinct ways to study a topic, and from a managerial standpoint, a focus on application is the preferred way. Serious reflection about this problem may suggest a somewhat different approach, however. Consider the following situation.

As a personnel manager for a medium-sized firm, you have been asked to discover why employee turnover in your firm is so high. Your boss has told you that it is your responsibility to assess this problem and then to offer suggestions aimed at reducing turnover. What will you do? Several possible strategies come to mind:

Talk with those who have quit the organization.
Talk with those who remain.
Talk to the employees’ supervisors.
Consult with personnel managers in other companies.
Measure job satisfaction.
Examine company policies and practices.
Examine the jobs where most turnover occurs.

None of these actions will likely be very successful in helping you arrive at sound conclusions, however. Talking with those who have left usually yields a variety of biased responses by those who either want to “get back at” the company or who fear that criticism will negatively affect their chances for future recommendations. Talking with those still employed has similar problems: why should they be candid and jeopardize their jobs? Talking with supervisors will not help if they themselves are the problem. Asking other personnel managers, while comforting, ignores major differences between organizations. Measuring job satisfaction, examining company policies, or examining the jobs themselves may help if one is fortunate enough to hit upon the right problem, but the probability of doing so is minimal. In short, many of the most obvious ways a manager can choose to solve a problem may yield biased results at best, and possibly no results at all.

A more viable approach would be to view the situation from a research standpoint and to use accepted methods of scientific inquiry to arrive at a solution that minimizes biased results. Most of what we know about organizational behavior results from efforts to apply such methods in solving organizational problems (e.g., How do we motivate employees? How do we develop effective leaders? How do we reduce stress at work?). An awareness of the nature of scientific inquiry is useful for understanding how we learned what we know about organizations as well as in facilitating efforts to solve behavioral problems at work.

Theory Building in Organizations

Briefly stated, a theory is a set of statements that serves to explain the manner in which certain concepts or variables are related. These statements result both from our present level of knowledge on the topic and from our assumptions about the variables themselves. The theory allows us to deduce logical propositions, or hypotheses, that can be tested in the field or laboratory. In short, a theory is a technique that helps us understand how variables fit together. Their use in research and in management is invaluable.

Uses of a Theory

Why do we have theories in the study of organizational behavior? First, theories help us organize knowledge about a given subject into a pattern of relationships that lends meaning to a series of observed events. They provide a structure for understanding. For instance, rather than struggling with a lengthy list of factors found to relate to employee turnover, a theory of turnover might suggest how such factors fit together and are related.

Second, theories help us to summarize diverse findings so that we can focus on major relationships and not get bogged down in details. A theory “permits us to handle large amounts of empirical data with relatively few propositions.” [M. E. Shaw and P. R. Costanzo, Theories of Social Psychology (New York: McGraw-Hill, 1970), p. 9.]

Finally, theories are useful in that they point the way to future research efforts. They raise new questions and suggest answers. In this sense, they serve a useful heuristic value in helping to differentiate between important and trivial questions for future research. Theories are useful both for the study and for the management of organizations. As Kurt Lewin said, “There is nothing so practical as a good theory.”

What Is a Good Theory?

Abraham Kaplan discusses in detail the criteria for evaluating the utility or soundness of a theory. [A. Kaplan, The Conduct of Inquiry (San Francisco: Chandler, 1964).] At least five such criteria can be mentioned:

Internal consistency. Are the propositions central to the theory free from contradiction? Are they logical?
External consistency. Are the propositions of a theory consistent with observations from real life?
Scientific parsimony. Does the theory contain only those concepts that are necessary to account for findings or to explain relationships? Simplicity of presentation is preferable unless added complexity furthers understanding or clarifies additional research findings.
Generalizability. In order for a theory to have much utility, it must apply to a wide range of situations or organizations. A theory of employee motivation that applies only to one company hardly helps us understand motivational processes or apply such knowledge elsewhere.
Verification. A good theory presents propositions that can be tested. Without an ability to operationalize the variables and subject the theory to field or laboratory testing, we are unable to determine its accuracy or utility.

To the extent that a theory satisfies these requirements, its usefulness both to researchers and to managers is enhanced. However, a theory is only a starting point. On the basis of theory, researchers and problem solvers can proceed to design studies aimed at verifying and refining the theories themselves. These studies must proceed according to commonly accepted principles of the scientific method.

Scientific Method in Organizational Behavior Research

Cohen and Nagel suggested that there are four basic “ways of knowing.” [M. Cohen and E. Nagel, An Introduction to Logic and Scientific Inquiry (New York: Harcourt, Brace and Company, 1943). See also E. Lawler, A. Mohrman, S. Mohrman, G. Ledford, and T. Cummings, Doing Research That Is Useful for Theory and Practice (San Francisco: Jossey-Bass, 1985).] Managers and researchers use all four of these techniques: tenacity, intuition, authority, and science. When managers form a belief (e.g., a happy worker is a productive worker) and continue to hold that belief out of habit and often in spite of contradictory information, they are using tenacity. They use intuition when they feel the answer is self-evident or when they have a hunch about how to solve a problem. They use authority when they seek an answer to a problem from an expert or consultant who supposedly has experience in the area. Finally, they use science —perhaps too seldom—when they are convinced that the three previous methods allow for too much subjectivity in interpretation.

In contrast to tenacity, intuition, and authority, the scientific method of inquiry “aims at knowledge that is objective in the sense of being intersubjectively certifiable, independent of individual opinion or preference, on the basis of data obtainable by suitable experiments or observations.” [C. G. Hempel, Aspects of Scientific Explanation (New York: The Free Press, 1965), p. 141.] In other words, the scientific approach to problem-solving sets some fairly rigorous standards in an attempt to substitute objectivity for subjectivity.

The scientific method in organizational behavior consists of four stages: (1) observation of the phenomena (facts) in the real world, (2) formulation of explanations for such phenomena using the inductive process, (3) generation of predictions or hypotheses about the phenomena using the deductive process, and (4) verification of the predictions or hypotheses using systematic, controlled observation. This process is shown in Exhibit A1 . When this rather abstract description of the steps of scientific inquiry is shown within the framework of an actual research study, the process becomes much clearer. A basic research paradigm is shown in Exhibit A2 . In essence, a scientific approach to research requires that the investigator or manager first recognize clearly what research questions are being posed. To paraphrase Lewis Carroll, if you don’t know where you’re going, any road will take you there. Many managers identify what they think is a problem (e.g., turnover) only to discover later that their “problem” turnover rate is much lower than that in comparable industries. Other managers look at poor employee morale or performance and ignore what may be the real problem (e.g., poor leadership).

On the basis of the research questions, specific hypotheses are identified. These hypotheses represent best guesses about what we expect to find. We set forth hypotheses to determine if we can predict the right answer so we can select a study design that allows for suitable testing. On the basis of the study design (to be discussed shortly), we observe the variables under study, analyze the data we collect, and draw relevant conclusions and management implications. When we follow this process, the risks of being guided by our own opinions or prejudices are minimized, and we arrive at useful answers to our original research questions.

Basic Research Designs

Although a detailed discussion of the various research designs is beyond the scope of this Appendix, we can review several common research designs that have been used to collect data in the study of people at work. Specifically, we will examine five different research designs that are frequently used to study behavior at work: (1) naturalistic observation, (2) survey research, (3) field study, (4) field experiment, and (5) laboratory experiment. In general, the level of rigor of the design increases as we move from naturalistic observation toward laboratory study. Unfortunately, so do the costs, in many cases.

Criteria for Evaluating Research Designs

Before examining the five designs, it will be helpful to consider how a researcher selects from among the various designs. Clearly, no one strategy or design is superior in all cases. Each has its place, depending upon the research goals and the constraints placed on the research.

However, when choosing among the potential designs, researchers generally must consider several things. For example, does the design require that you specify hypotheses a priori? If you specify appropriate hypotheses and are able to confirm them, then you can predict behavior in organizations. As a manager, being able to predict behavior in advance allows you to intervene and make necessary changes to remedy problem situations. The ability to accurately predict behavior is clearly superior to simply being able to explain behavior after the fact.

Other factors to examine are the method of measurement and the degree of control to be used. Does the method of measurement use qualitative or quantitative measures? Although qualitative measures may be useful for generating future hypotheses, quantitative measures add more perceived rigor to results. Also, if you are interested in demonstrating casual relationships, it is necessary to have a high degree of control over the study variables. You must be able to manipulate the primary study variable to determine the results of this manipulation while at the same time keeping other potentially contaminating variables constant so they do not interfere in the results.

In addition, a researcher must know to what extent they can generalize the results from the study to apply to other organizations or situations. Results that are situation-specific are of little use to managers. External validity is of key importance. And, of course, in practical terms, how much is it going to cost to carry out the study and discover a solution? Cost can be measured in many ways, including time and money.

The analysis of the previous five criteria provides insight concerning the overall level of rigor of the research design. The more rigorous the design, the more confidence one has in the results. This is because more rigorous designs typically employ more accurate measures or interventions and attempt to control for contaminating influences on study results. With this in mind, we can now consider various research designs.

Naturalistic Observation

Naturalistic observations represent the most primitive (least rigorous) method of research in organizations. Simply put, naturalistic observations represent conclusions drawn from observing events. At least two forms of such research can be identified: (1) authoritative opinions and (2) case studies.

Authoritative opinions are the opinions of experts in the field. When Henri Fayol wrote his early works on management, for example, he was offering his advice as a former industrial manager. On the basis of experience in real work situations, Fayol and others suggest that what they have learned can be applied to a variety of work organizations with relative ease. Other examples of authoritative opinions can be found in Barnard’s The Functions of the Executive, Sloan’s My Years with General Motors, and Peters and Waterman’s In Search of Excellence. Throughout their works, these writers attempt to draw lessons from their own practical experience that can help other managers assess their problems.

The second use of naturalistic observation can be seen in the case study. Case studies attempt to take one situation in one organization and to analyze it in detail with regard to the interpersonal dynamics among the various members. For instance, we may have a case of one middle manager who appears to have burned out on the job; his performance seems to have reached a plateau. The case would then review the cast of characters in the situation and how each one related to this manager’s problem. Moreover, the case would review any actions that were taken to remedy the problem. Throughout, emphasis would be placed on what managers could learn from this one real-life problem that can possibly relate to other situations.

Survey Research

Many times, managers wish to know something about the extent to which employees are satisfied with their jobs, are loyal to the organization, or experience stress on the job. In such cases, the managers (or the researchers) are interested mainly in considering quantitative values of the responses. Questionnaires designed to measure such variables are examples of survey research. Here we are not attempting to relate results to subsequent events. We simply wish to assess the general feelings and attitudes of employees.

Surveys are particularly popular with managers today as a method of assessing relative job attitudes. Hence, we may make an annual attitude survey and track changes in attitudes over time. If attitudes begin to decline, management is alerted to the problem and can take steps to remedy the situation.

Field Study

In a field study, the researcher is interested in the relationship between a predictor variable (e.g., job satisfaction) and a subsequent criterion variable (e.g., employee turnover or performance). Measures of each variable are taken (satisfaction, perhaps through a questionnaire, and turnover, from company records) and are compared to determine the extent of correlation. No attempt is made to intervene in the system or to manipulate any of the variables, as is the case with experimental approaches.

To continue the simple example we began with, a manager may have a hypothesis that says that satisfaction is a primary indicator of employee turnover. After measuring both, it is found that there is a moderate relationship between the two variables. Hence, the manager may conclude that the two are probably related. Even so, because of the moderate nature of the relationship, it is clear that other factors also influence turnover; otherwise, there would be a much stronger relationship. The manager concludes that, although efforts to improve job satisfaction may help solve the problem, other influences on turnover must also be looked at, such as salary level and supervisory style.

Field Experiment

A field experiment is much like a field study, with one important exception. Instead of simply measuring job satisfaction, the manager or researcher makes efforts to actually change satisfaction levels. In an experiment, we attempt to manipulate the predictor variable. This is most often done by dividing the sample into two groups: an experimental group and a control group. In the experimental group, we intervene and introduce a major change. Perhaps we alter the compensation program or give supervisors some human relations training. The control group receives no such treatment. After a time, we compare turnover rates in the two groups. If we have identified the correct treatment (that is, a true influence on turnover), turnover rates would be reduced in the experimental group but not in the control group.

In other words, in a field experiment, as opposed to a field study, we intentionally change one aspect of the work environment in the experimental group and compare the impact of the change with the untreated control group. Thus, we can be relatively assured that the solution we have identified is, in fact, a true predictor variable and is of use to management.

Laboratory Experiment

Up to this point, we have considered a variety of research designs that all make use of the actual work environment, the field. In this last design, laboratory experiments, we employ the same level of rigor as that of the field experiment and actually manipulate the predictor variable, but we do so in an artificial environment instead of a real one.

We might, for instance, wish to study the effects of various compensation programs (hourly rate versus piece rate) on performance. To do this, we might employ two groups of business students and have both groups work on a simulated work exercise. In doing so, we are simulating a real work situation. Each group would then be paid differently. After the experiment, an assessment would be made of the impact of the two compensation plans on productivity.

Comparing Research Designs

Now that we have reviewed various research designs, we might wonder which designs are best. This is not an easy call. All designs have been used by managers and researchers in studying problems of people at work. Perhaps the question can best be answered by considering the relative strengths and weaknesses of each, on the basis of our earlier discussion of the criteria for evaluating research designs (see Table A1 ). We should then have a better idea of which design or designs would be appropriate for a particular problem or situation.

Specification of Hypotheses in Advance. It was noted earlier that the ability to specify a priori hypotheses adds rigor to the study. In general, hypotheses are not given for naturalistic observations or survey research. These two techniques are used commonly for exploratory analyses and for identifying pertinent research questions for more rigorous future study. On the other hand, the remaining three designs (field study, field experiment, and laboratory experiment) do allow explicitly for a priori hypotheses. Hence, they are superior in this sense.

Qualitative versus Quantitative Measures. Naturalistic observations typically involve qualitative data, whereas field studies and both forms of experiment typically involve quantitative data. Survey research most often provides for both. Hence, if it is important to collect hard data concerning a problem (e.g., what is the magnitude of the relationship between satisfaction and turnover?), quantitative designs are to be preferred. On the other hand, if one is more concerned about identifying major reasons for turnover and little prior knowledge about the problem exists, qualitative data may be preferred, and survey research may be a better research strategy. The selection of an appropriate design hinges in part on the intended uses for the information.

Control. As noted earlier, control represents the extent to which potentially contaminating influences can be minimized in a study. Clearly, experimental procedures allow for better control than do nonexperimental ones. The researcher or manager can systematically structure the desired work environment and minimize irrelevant or contaminating influences. As a result, conclusions concerning causal relations between variables can be made with some degree of certainty. Where it is not possible to secure such high control, however—perhaps because the organization does not wish to make a major structural change simply for purposes of an experiment—a field study represents a compromise design. It allows for some degree of control but does not require changing the organization.

External Validity. The question of external validity is crucial to any study. If the results of a study in one setting cannot be applied with confidence to other settings, the utility of the results for managers is limited. In this regard, survey research, field studies, and field experiments have higher levels of external validity than naturalistic observations or laboratory experiments. Naturalistic observations are typically based on nonrandom samples, and such samples often exhibit characteristics that may not allow for transfers of learning from one organization to another. A clear example can be seen in the case of a small company in which the president implemented a unique compensation plan that proved successful. It would be impossible to predict whether such a plan would work in a major corporation, because of the different nature of the organizations. Similarly, there is some question about how realistic a work environment is actually created in a laboratory situation. If managers are to learn from the lessons of other organizations, they should first learn the extent to which the findings from one kind of organization are applicable elsewhere.

Cost. As one would expect, the quality of information and its price covary. The more rigorous the design (and thus the more accurate the information), the higher the cost. Costs can be incurred in a variety of ways and include actual out-of-pocket expenses, time invested, and residue costs. The organization is left with the aftermath of an experiment, which could mean raised employee expectations and anxieties, as well as the possibility of disillusionment if the experiment fails. It should be noted that, in survey research, a large amount of general information can be gathered quickly and cheaply.

Overall Level of Rigor. In summary, then, the real answer to the question concerning which strategy is best lies in the degrees of freedom a manager has in selecting the design. If an experiment is clearly out of the question (perhaps because one’s superior doesn’t want anything altered), a field study may be the best possible strategy, given the constraints. In fact, field studies are often considered a good compromise strategy in that they have a medium amount of rigor but are also fairly quick and inexpensive. On the other hand, if one simply wishes to take an attitude survey, survey research is clearly in order. If one is not allowed to do anything, authoritative opinions from others may be the only information available. However, if constraints are not severe, experimental methods are clearly superior in that they allow for greater certainty concerning major influences on the criterion variable and on the problem itself.

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Book title: Organizational Behavior
Publication date: Jun 5, 2019
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Book URL: https://openstax.org/books/organizational-behavior/pages/1-introduction
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What is Operations Research? | NC State OR

What is Operations Research?

Last Updated: 08/16/2022 and all information on this page is accurate and up-to-date

The simple answer: Operations Research (OR) is a discipline of problem-solving and decision-making that uses advanced analytical methods to help management run an effective organization. Problems are broken down, analyzed and solved in steps.

Identify a problem
Build a model around the real-world problem
Use the model and data to arrive at solutions
Test the solution and analyze its success
Implement the solution

The technical answer: Operations research, also known as management sciences, uses scientific methodology to study systems whose design or operation requires human decision-making. OR provides the means for making the most effective systems design and operation decisions. The strength and versatility of OR stem from its diagnostic power through observation and modeling and its prescriptive power through analysis and synthesis.

OR is interdisciplinary, drawing on and contributing to the techniques from many fields, including mathematics and mathematical sciences, engineering, economics and the physical sciences. OR practitioners have successfully solved a wide variety of real-world problems, varying from the optimal design of telecommunications networks in uncertain demand to the planning for an optimal deployment of armed forces during wartime. Many new applications originate from current societal energy production and distribution problems, environmental pollution control, health maintenance, and software production.

The CEO of the Future is an Engineer

Studies show that three times as many S&P 500 CEOs hold undergraduate degrees in engineering rather than business administration. Operation research practitioners lead that trend among the next generation of engineers and scientists. They are tomorrow’s business leaders.

Operations Research Offers Workplace Freedom

Operations research practitioners have offices and work in the settings they are trying to improve. When collecting data, they may observe the staff working in a restaurant or watch workers assembling parts in a factory. When solving problems, they are in an office analyzing the data they or others have collected.

The World Needs more Operations Research

As companies battle in the competitive world market, the need for operations research practitioners grows. Why? They are the engineers trained to be productivity and quality improvement specialists. They share the common goal of saving companies money and increasing performance.

Operations Research is all about Options

Operations research practitioners work in almost any industry, anywhere in the world. They can work in and out of the office while interacting with people and processes they want to improve. This flexibility gives them a career advantage over other types of engineering. Operations research practitioners have the luxury of not specializing. They can keep their options open. This makes them immune to the ups and downs of any individual industry.

Careers in Operations Research

When considering a career in operations research, it’s logical to ask, Will I be able to get a job?” Answer: “YES”

Operation Research Continues to Grow

According to the Bureau of Labor, operations research employment will continue to grow by over 25 percent during the next decade. This is faster than the average for all occupations.

Companies look for new ways to reduce costs and raise productivity every day. They will turn to operation research practitioners to develop more efficient processes and reduce costs, delays and waste. This leads to job growth for these engineers, even in manufacturing industries with slow-growing or declining employment. Because their work is done in management, many operations research practitioners leave the occupation to become managers.

It is a great time to be an operations research practitioner. They solve problems and there’s never a shortage of those!

What is Industrial Engineering | NC State University

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In This Article Expand or collapse the "in this article" section Problem Solving and Decision Making

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General Approaches to Problem Solving
Representational Accounts
Problem Space and Search
Working Memory and Problem Solving
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The Rational Approach
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Problem Solving and Decision Making by Emily G. Nielsen , John Paul Minda LAST REVIEWED: 26 June 2019 LAST MODIFIED: 26 June 2019 DOI: 10.1093/obo/9780199828340-0246

Problem solving and decision making are both examples of complex, higher-order thinking. Both involve the assessment of the environment, the involvement of working memory or short-term memory, reliance on long term memory, effects of knowledge, and the application of heuristics to complete a behavior. A problem can be defined as an impasse or gap between a current state and a desired goal state. Problem solving is the set of cognitive operations that a person engages in to change the current state, to go beyond the impasse, and achieve a desired outcome. Problem solving involves the mental representation of the problem state and the manipulation of this representation in order to move closer to the goal. Problems can vary in complexity, abstraction, and how well defined (or not) the initial state and the goal state are. Research has generally approached problem solving by examining the behaviors and cognitive processes involved, and some work has examined problem solving using computational processes as well. Decision making is the process of selecting and choosing one action or behavior out of several alternatives. Like problem solving, decision making involves the coordination of memories and executive resources. Research on decision making has paid particular attention to the cognitive biases that account for suboptimal decisions and decisions that deviate from rationality. The current bibliography first outlines some general resources on the psychology of problem solving and decision making before examining each of these topics in detail. Specifically, this review covers cognitive, neuroscientific, and computational approaches to problem solving, as well as decision making models and cognitive heuristics and biases.

General Overviews

Current research in the area of problem solving and decision making is published in both general and specialized scientific journals. Theoretical and scholarly work is often summarized and developed in full-length books and chapter. These may focus on the subfields of problem solving and decision making or the larger field of thinking and higher-order cognition.

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Operations Research and Optimization Techniques

First Online: 01 January 2015

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Ali D. Haidar 2

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This chapter will look at the principles of operations research and quantitative methods that are most accessible and suitable for program managers. Operations research is, in principle, the application of scientific methods, techniques, and tools for solving problems involving the operations of a system in order to provide those in control of the system with optimum solutions to problems. Put simply, it is a systematic and analytical approach to decision making and problem solving. This chapter provides an overview of operations research, its approach to solving problems, and some examples of successful applications. From the standpoint of a program manager, operations research is a tool that can do a great deal to improve productivity, assist in decision making, and optimize solutions. Therefore, the potential rewards can be enormous. Optimization techniques are also explained in this chapter to help program managers understand their importance. The last part of the chapter will look at linear programming methods and applications for construction, as this is the most widely applicable field for these types of problems. Linear programming can be used to allocate, assign, schedule, select, or evaluate whatever possibilities limited resources possess for different jobs. It has been used extensively in construction-related problems, where it can deduce the most profitable methods of allocating resources.

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Aronson, J. E., & Zionts, S. (2008). Operations research: Methods, models, and applications . Paperback, April 28.

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Haidar, A.D. (2016). Operations Research and Optimization Techniques. In: Construction Program Management – Decision Making and Optimization Techniques. Springer, Cham. https://doi.org/10.1007/978-3-319-20774-2_5

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A Guide to Using the Scientific Method in Everyday Life

The scientific method —the process used by scientists to understand the natural world—has the merit of investigating natural phenomena in a rigorous manner. Working from hypotheses, scientists draw conclusions based on empirical data. These data are validated on large-scale numbers and take into consideration the intrinsic variability of the real world. For people unfamiliar with its intrinsic jargon and formalities, science may seem esoteric. And this is a huge problem: science invites criticism because it is not easily understood. So why is it important, then, that every person understand how science is done?

Because the scientific method is, first of all, a matter of logical reasoning and only afterwards, a procedure to be applied in a laboratory.

Individuals without training in logical reasoning are more easily victims of distorted perspectives about themselves and the world. An example is represented by the so-called “ cognitive biases ”—systematic mistakes that individuals make when they try to think rationally, and which lead to erroneous or inaccurate conclusions. People can easily overestimate the relevance of their own behaviors and choices. They can lack the ability to self-estimate the quality of their performances and thoughts . Unconsciously, they could even end up selecting only the arguments that support their hypothesis or beliefs . This is why the scientific framework should be conceived not only as a mechanism for understanding the natural world, but also as a framework for engaging in logical reasoning and discussion.

A brief history of the scientific method

The scientific method has its roots in the sixteenth and seventeenth centuries. Philosophers Francis Bacon and René Descartes are often credited with formalizing the scientific method because they contrasted the idea that research should be guided by metaphysical pre-conceived concepts of the nature of reality—a position that, at the time, was highly supported by their colleagues . In essence, Bacon thought that inductive reasoning based on empirical observation was critical to the formulation of hypotheses and the generation of new understanding : general or universal principles describing how nature works are derived only from observations of recurring phenomena and data recorded from them. The inductive method was used, for example, by the scientist Rudolf Virchow to formulate the third principle of the notorious cell theory , according to which every cell derives from a pre-existing one. The rationale behind this conclusion is that because all observations of cell behavior show that cells are only derived from other cells, this assertion must be always true.

Inductive reasoning, however, is not immune to mistakes and limitations. Referring back to cell theory, there may be rare occasions in which a cell does not arise from a pre-existing one, even though we haven’t observed it yet—our observations on cell behavior, although numerous, can still benefit from additional observations to either refute or support the conclusion that all cells arise from pre-existing ones. And this is where limited observations can lead to erroneous conclusions reasoned inductively. In another example, if one never has seen a swan that is not white, they might conclude that all swans are white, even when we know that black swans do exist, however rare they may be.

The universally accepted scientific method, as it is used in science laboratories today, is grounded in hypothetico-deductive reasoning . Research progresses via iterative empirical testing of formulated, testable hypotheses (formulated through inductive reasoning). A testable hypothesis is one that can be rejected (falsified) by empirical observations, a concept known as the principle of falsification . Initially, ideas and conjectures are formulated. Experiments are then performed to test them. If the body of evidence fails to reject the hypothesis, the hypothesis stands. It stands however until and unless another (even singular) empirical observation falsifies it. However, just as with inductive reasoning, hypothetico-deductive reasoning is not immune to pitfalls—assumptions built into hypotheses can be shown to be false, thereby nullifying previously unrejected hypotheses. The bottom line is that science does not work to prove anything about the natural world. Instead, it builds hypotheses that explain the natural world and then attempts to find the hole in the reasoning (i.e., it works to disprove things about the natural world).

How do scientists test hypotheses?

Controlled experiments

The word “experiment” can be misleading because it implies a lack of control over the process. Therefore, it is important to understand that science uses controlled experiments in order to test hypotheses and contribute new knowledge. So what exactly is a controlled experiment, then?

Let us take a practical example. Our starting hypothesis is the following: we have a novel drug that we think inhibits the division of cells, meaning that it prevents one cell from dividing into two cells (recall the description of cell theory above). To test this hypothesis, we could treat some cells with the drug on a plate that contains nutrients and fuel required for their survival and division (a standard cell biology assay). If the drug works as expected, the cells should stop dividing. This type of drug might be useful, for example, in treating cancers because slowing or stopping the division of cells would result in the slowing or stopping of tumor growth.

Although this experiment is relatively easy to do, the mere process of doing science means that several experimental variables (like temperature of the cells or drug, dosage, and so on) could play a major role in the experiment. This could result in a failed experiment when the drug actually does work, or it could give the appearance that the drug is working when it is not. Given that these variables cannot be eliminated, scientists always run control experiments in parallel to the real ones, so that the effects of these other variables can be determined. Control experiments are designed so that all variables, with the exception of the one under investigation, are kept constant. In simple terms, the conditions must be identical between the control and the actual experiment.

Coming back to our example, when a drug is administered it is not pure. Often, it is dissolved in a solvent like water or oil. Therefore, the perfect control to the actual experiment would be to administer pure solvent (without the added drug) at the same time and with the same tools, where all other experimental variables (like temperature, as mentioned above) are the same between the two (Figure 1). Any difference in effect on cell division in the actual experiment here can be attributed to an effect of the drug because the effects of the solvent were controlled.

In order to provide evidence of the quality of a single, specific experiment, it needs to be performed multiple times in the same experimental conditions. We call these multiple experiments “replicates” of the experiment (Figure 2). The more replicates of the same experiment, the more confident the scientist can be about the conclusions of that experiment under the given conditions. However, multiple replicates under the same experimental conditions are of no help when scientists aim at acquiring more empirical evidence to support their hypothesis. Instead, they need independent experiments (Figure 3), in their own lab and in other labs across the world, to validate their results.

Often times, especially when a given experiment has been repeated and its outcome is not fully clear, it is better to find alternative experimental assays to test the hypothesis.

Applying the scientific approach to everyday life

So, what can we take from the scientific approach to apply to our everyday lives?

A few weeks ago, I had an agitated conversation with a bunch of friends concerning the following question: What is the definition of intelligence?

Defining “intelligence” is not easy. At the beginning of the conversation, everybody had a different, “personal” conception of intelligence in mind, which – tacitly – implied that the conversation could have taken several different directions. We realized rather soon that someone thought that an intelligent person is whoever is able to adapt faster to new situations; someone else thought that an intelligent person is whoever is able to deal with other people and empathize with them. Personally, I thought that an intelligent person is whoever displays high cognitive skills, especially in abstract reasoning.

The scientific method has the merit of providing a reference system, with precise protocols and rules to follow. Remember: experiments must be reproducible, which means that an independent scientists in a different laboratory, when provided with the same equipment and protocols, should get comparable results. Fruitful conversations as well need precise language, a kind of reference vocabulary everybody should agree upon, in order to discuss about the same “content”. This is something we often forget, something that was somehow missing at the opening of the aforementioned conversation: even among friends, we should always agree on premises, and define them in a rigorous manner, so that they are the same for everybody. When speaking about “intelligence”, we must all make sure we understand meaning and context of the vocabulary adopted in the debate (Figure 4, point 1). This is the first step of “controlling” a conversation.

There is another downside that a discussion well-grounded in a scientific framework would avoid. The mistake is not structuring the debate so that all its elements, except for the one under investigation, are kept constant (Figure 4, point 2). This is particularly true when people aim at making comparisons between groups to support their claim. For example, they may try to define what intelligence is by comparing the achievements in life of different individuals: “Stephen Hawking is a brilliant example of intelligence because of his great contribution to the physics of black holes”. This statement does not help to define what intelligence is, simply because it compares Stephen Hawking, a famous and exceptional physicist, to any other person, who statistically speaking, knows nothing about physics. Hawking first went to the University of Oxford, then he moved to the University of Cambridge. He was in contact with the most influential physicists on Earth. Other people were not. All of this, of course, does not disprove Hawking’s intelligence; but from a logical and methodological point of view, given the multitude of variables included in this comparison, it cannot prove it. Thus, the sentence “Stephen Hawking is a brilliant example of intelligence because of his great contribution to the physics of black holes” is not a valid argument to describe what intelligence is. If we really intend to approximate a definition of intelligence, Steven Hawking should be compared to other physicists, even better if they were Hawking’s classmates at the time of college, and colleagues afterwards during years of academic research.

In simple terms, as scientists do in the lab, while debating we should try to compare groups of elements that display identical, or highly similar, features. As previously mentioned, all variables – except for the one under investigation – must be kept constant.

This insightful piece presents a detailed analysis of how and why science can help to develop critical thinking.

In a nutshell

Here is how to approach a daily conversation in a rigorous, scientific manner:

First discuss about the reference vocabulary, then discuss about the content of the discussion. Think about a researcher who is writing down an experimental protocol that will be used by thousands of other scientists in varying continents. If the protocol is rigorously written, all scientists using it should get comparable experimental outcomes. In science this means reproducible knowledge, in daily life this means fruitful conversations in which individuals are on the same page.
Adopt “controlled” arguments to support your claims. When making comparisons between groups, visualize two blank scenarios. As you start to add details to both of them, you have two options. If your aim is to hide a specific detail, the better is to design the two scenarios in a completely different manner—it is to increase the variables. But if your intention is to help the observer to isolate a specific detail, the better is to design identical scenarios, with the exception of the intended detail—it is therefore to keep most of the variables constant. This is precisely how scientists ideate adequate experiments to isolate new pieces of knowledge, and how individuals should orchestrate their thoughts in order to test them and facilitate their comprehension to others.

Not only the scientific method should offer individuals an elitist way to investigate reality, but also an accessible tool to properly reason and discuss about it.

Edited by Jason Organ, PhD, Indiana University School of Medicine.

Simone is a molecular biologist on the verge of obtaining a doctoral title at the University of Ulm, Germany. He is Vice-Director at Culturico (https://culturico.com/), where his writings span from Literature to Sociology, from Philosophy to Science. His writings recently appeared in Psychology Today, openDemocracy, Splice Today, Merion West, Uncommon Ground and The Society Pages. Follow Simone on Twitter: @simredaelli

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This has to be the best article I have ever read on Scientific Thinking. I am presently writing a treatise on how Scientific thinking can be adopted to entreat all situations.And how, a 4 year old child can be taught to adopt Scientific thinking, so that, the child can look at situations that bothers her and she could try to think about that situation by formulating the right questions. She may not have the tools to find right answers? But, forming questions by using right technique ? May just make her find a way to put her mind to rest even at that level. That is why, 4 year olds are often “eerily: (!)intelligent, I have iften been intimidated and plain embarrassed to see an intelligent and well spoken 4 year old deal with celibrity ! Of course, there are a lot of variables that have to be kept in mind in order to train children in such controlled thinking environment, as the screenplay of little Sheldon shows. Thanking the author with all my heart – #ershadspeak #wearescience #weareallscientists Ershad Khandker

Simone, thank you for this article. I have the idea that I want to apply what I learned in Biology to everyday life. You addressed this issue, and have given some basic steps in using the scientific method.

Scientific Method Steps in Psychology Research

Steps, Uses, and Key Terms

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

Verywell / Theresa Chiechi

How do researchers investigate psychological phenomena? They utilize a process known as the scientific method to study different aspects of how people think and behave.

When conducting research, the scientific method steps to follow are:

Observe what you want to investigate
Ask a research question and make predictions
Test the hypothesis and collect data
Examine the results and draw conclusions
Report and share the results

This process not only allows scientists to investigate and understand different psychological phenomena but also provides researchers and others a way to share and discuss the results of their studies.

Generally, there are five main steps in the scientific method, although some may break down this process into six or seven steps. An additional step in the process can also include developing new research questions based on your findings.

What Is the Scientific Method?

What is the scientific method and how is it used in psychology?

The scientific method consists of five steps. It is essentially a step-by-step process that researchers can follow to determine if there is some type of relationship between two or more variables.

By knowing the steps of the scientific method, you can better understand the process researchers go through to arrive at conclusions about human behavior.

Scientific Method Steps

While research studies can vary, these are the basic steps that psychologists and scientists use when investigating human behavior.

The following are the scientific method steps:

Step 1. Make an Observation

Before a researcher can begin, they must choose a topic to study. Once an area of interest has been chosen, the researchers must then conduct a thorough review of the existing literature on the subject. This review will provide valuable information about what has already been learned about the topic and what questions remain to be answered.

A literature review might involve looking at a considerable amount of written material from both books and academic journals dating back decades.

The relevant information collected by the researcher will be presented in the introduction section of the final published study results. This background material will also help the researcher with the first major step in conducting a psychology study: formulating a hypothesis.

Step 2. Ask a Question

Once a researcher has observed something and gained some background information on the topic, the next step is to ask a question. The researcher will form a hypothesis, which is an educated guess about the relationship between two or more variables

For example, a researcher might ask a question about the relationship between sleep and academic performance: Do students who get more sleep perform better on tests at school?

In order to formulate a good hypothesis, it is important to think about different questions you might have about a particular topic.

You should also consider how you could investigate the causes. Falsifiability is an important part of any valid hypothesis. In other words, if a hypothesis was false, there needs to be a way for scientists to demonstrate that it is false.

Step 3. Test Your Hypothesis and Collect Data

Once you have a solid hypothesis, the next step of the scientific method is to put this hunch to the test by collecting data. The exact methods used to investigate a hypothesis depend on exactly what is being studied. There are two basic forms of research that a psychologist might utilize: descriptive research or experimental research.

Descriptive research is typically used when it would be difficult or even impossible to manipulate the variables in question. Examples of descriptive research include case studies, naturalistic observation , and correlation studies. Phone surveys that are often used by marketers are one example of descriptive research.

Correlational studies are quite common in psychology research. While they do not allow researchers to determine cause-and-effect, they do make it possible to spot relationships between different variables and to measure the strength of those relationships.

Experimental research is used to explore cause-and-effect relationships between two or more variables. This type of research involves systematically manipulating an independent variable and then measuring the effect that it has on a defined dependent variable .

One of the major advantages of this method is that it allows researchers to actually determine if changes in one variable actually cause changes in another.

While psychology experiments are often quite complex, a simple experiment is fairly basic but does allow researchers to determine cause-and-effect relationships between variables. Most simple experiments use a control group (those who do not receive the treatment) and an experimental group (those who do receive the treatment).

Step 4. Examine the Results and Draw Conclusions

Once a researcher has designed the study and collected the data, it is time to examine this information and draw conclusions about what has been found. Using statistics , researchers can summarize the data, analyze the results, and draw conclusions based on this evidence.

So how does a researcher decide what the results of a study mean? Not only can statistical analysis support (or refute) the researcher’s hypothesis; it can also be used to determine if the findings are statistically significant.

When results are said to be statistically significant, it means that it is unlikely that these results are due to chance.

Based on these observations, researchers must then determine what the results mean. In some cases, an experiment will support a hypothesis, but in other cases, it will fail to support the hypothesis.

So what happens if the results of a psychology experiment do not support the researcher's hypothesis? Does this mean that the study was worthless?

Just because the findings fail to support the hypothesis does not mean that the research is not useful or informative. In fact, such research plays an important role in helping scientists develop new questions and hypotheses to explore in the future.

After conclusions have been drawn, the next step is to share the results with the rest of the scientific community. This is an important part of the process because it contributes to the overall knowledge base and can help other scientists find new research avenues to explore.

Step 5. Report the Results

The final step in a psychology study is to report the findings. This is often done by writing up a description of the study and publishing the article in an academic or professional journal. The results of psychological studies can be seen in peer-reviewed journals such as Psychological Bulletin , the Journal of Social Psychology , Developmental Psychology , and many others.

The structure of a journal article follows a specified format that has been outlined by the American Psychological Association (APA) . In these articles, researchers:

Provide a brief history and background on previous research
Present their hypothesis
Identify who participated in the study and how they were selected
Provide operational definitions for each variable
Describe the measures and procedures that were used to collect data
Explain how the information collected was analyzed
Discuss what the results mean

Why is such a detailed record of a psychological study so important? By clearly explaining the steps and procedures used throughout the study, other researchers can then replicate the results. The editorial process employed by academic and professional journals ensures that each article that is submitted undergoes a thorough peer review, which helps ensure that the study is scientifically sound.

Once published, the study becomes another piece of the existing puzzle of our knowledge base on that topic.

Before you begin exploring the scientific method steps, here's a review of some key terms and definitions that you should be familiar with:

Falsifiable : The variables can be measured so that if a hypothesis is false, it can be proven false
Hypothesis : An educated guess about the possible relationship between two or more variables
Variable : A factor or element that can change in observable and measurable ways
Operational definition : A full description of exactly how variables are defined, how they will be manipulated, and how they will be measured

Uses for the Scientific Method

The goals of psychological studies are to describe, explain, predict and perhaps influence mental processes or behaviors. In order to do this, psychologists utilize the scientific method to conduct psychological research. The scientific method is a set of principles and procedures that are used by researchers to develop questions, collect data, and reach conclusions.

Goals of Scientific Research in Psychology

Researchers seek not only to describe behaviors and explain why these behaviors occur; they also strive to create research that can be used to predict and even change human behavior.

Psychologists and other social scientists regularly propose explanations for human behavior. On a more informal level, people make judgments about the intentions, motivations , and actions of others on a daily basis.

While the everyday judgments we make about human behavior are subjective and anecdotal, researchers use the scientific method to study psychology in an objective and systematic way. The results of these studies are often reported in popular media, which leads many to wonder just how or why researchers arrived at the conclusions they did.

Examples of the Scientific Method

Now that you're familiar with the scientific method steps, it's useful to see how each step could work with a real-life example.

Say, for instance, that researchers set out to discover what the relationship is between psychotherapy and anxiety .

Step 1. Make an observation : The researchers choose to focus their study on adults ages 25 to 40 with generalized anxiety disorder.
Step 2. Ask a question : The question they want to answer in their study is: Do weekly psychotherapy sessions reduce symptoms in adults ages 25 to 40 with generalized anxiety disorder?
Step 3. Test your hypothesis : Researchers collect data on participants' anxiety symptoms . They work with therapists to create a consistent program that all participants undergo. Group 1 may attend therapy once per week, whereas group 2 does not attend therapy.
Step 4. Examine the results : Participants record their symptoms and any changes over a period of three months. After this period, people in group 1 report significant improvements in their anxiety symptoms, whereas those in group 2 report no significant changes.
Step 5. Report the results : Researchers write a report that includes their hypothesis, information on participants, variables, procedure, and conclusions drawn from the study. In this case, they say that "Weekly therapy sessions are shown to reduce anxiety symptoms in adults ages 25 to 40."

Of course, there are many details that go into planning and executing a study such as this. But this general outline gives you an idea of how an idea is formulated and tested, and how researchers arrive at results using the scientific method.

Erol A. How to conduct scientific research ? Noro Psikiyatr Ars . 2017;54(2):97-98. doi:10.5152/npa.2017.0120102

University of Minnesota. Psychologists use the scientific method to guide their research .

Shaughnessy, JJ, Zechmeister, EB, & Zechmeister, JS. Research Methods In Psychology . New York: McGraw Hill Education; 2015.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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5 Key Decision-Making Techniques for Managers

Business manager engaging in decision-making with his team

31 Mar 2020

Decision-making is an essential business skill that drives organizational performance. A survey of more than 750 companies by management consulting firm Bain found a 95 percent correlation between decision-making effectiveness and financial results. The data also showed companies that excel at making and executing strategic decisions generate returns nearly six percent higher than those of their competitors.

At many organizations, it’s up to managers to make the key decisions that influence business strategy. Research by consulting firm McKinsey , however, shows that 61 percent of them believe at least half the time they spend doing so is ineffective.

If you want to avoid falling into this demographic, here are five decision-making techniques you can employ to improve your management skills and help your organization succeed.

Access your free e-book today.

Decision-Making Techniques for Managers

1. take a process-oriented approach.

One of your primary responsibilities as a manager is to get things done with and through others, which involves leveraging organizational processes to accomplish goals and produce results. According to Harvard Business School Professor Len Schlesinger, who’s featured in the online course Management Essentials , decision-making is one of the processes you can use to your advantage.

“The majority of people think about making decisions as an event,” Schlesinger says. “It’s very rare to find a single point in time where a ‘decision of significance’ is made and things go forward from there. What we’re really talking about is a process. The role of the manager in overseeing that process is straightforward, yet, at the same time, extraordinarily complex.”

When establishing your decision-making process , first frame the issue at hand to ensure you ask the right questions and everyone agrees on what needs to be decided. From there, build your team and manage group dynamics to analyze the problem and craft a viable solution. By following a structured, multi-step process, you can make informed decisions and achieve the desired outcome.

2. Involve Your Team in the Process

Decision-making doesn’t have to be done in a vacuum. To avoid relying on managerial decisions alone, involve your team in the process to bring multiple viewpoints into the conversation and stimulate creative problem-solving .

Research in the journal Royal Society Open Science shows team decision-making is highly effective because it pools individuals’ collective knowledge and experience, leading to more innovative solutions and helping to surface and overcome hidden biases among groups.

Considering others’ perspectives on how to approach and surmount a specific challenge is an ideal alternative because it helps you become more aware of your implicit biases and manage your team with greater emotional intelligence .

Related: Emotional Intelligence Skills: What They Are & How to Develop Them

3. Foster a Collaborative Mindset

Fostering the right mindset early in the decision-making process is critical to ensuring your team works collaboratively—not contentiously.

When facing a decision, there are two key mindsets to consider:

Decision-Making Mindsets: Advocacy vs. Inquiry

Advocacy: A mindset that regards decision-making as a contest. In a group with an advocacy mindset, individuals try to persuade others, defend their positions, and downplay their weaknesses.
Inquiry: A mindset that navigates decision-making with collaborative problem-solving. An inquiry mindset centers on individuals testing and evaluating assumptions by presenting balanced arguments, considering alternatives, and being open to constructive criticism.

“On the surface, advocacy and inquiry approaches look deceptively similar,” HBS Professor David Garvin says in Management Essentials . “Both involve individuals engaged in debates, drawing on data, developing alternatives, and deciding on future directions. But, despite these similarities, inquiry and advocacy produce very different results.”

A study by software company Cloverpop found that decisions made and executed by diverse teams deliver 60 percent better results. Strive to instill your team members with an inquiry mindset so they’re empowered to think critically and feel their perspectives are welcomed and valued rather than discouraged and dismissed.

4. Create and Uphold Psychological Safety

For your team members to feel comfortable sharing their diverse perspectives and working collaboratively, it’s crucial to create and maintain a psychologically safe environment. According to research by technology company Google , psychological safety is the most important dynamic found among high-performing teams.

“Psychological safety is essential—first and foremost—for getting the information and perspectives out,” HBS Professor Amy Edmondson says in Management Essentials . “It’s helpful to be able to talk about what we know and think in an effective and thoughtful way before coming to a final conclusion.”

To help your team feel psychologically safe, be respectful and give fair consideration when listening to everyone’s opinions. When voicing your own point of view, be open and transparent, and adapt your communication style to meet the group’s needs. By actively listening and being attuned to your colleagues’ emotions and attitudes, you can forge a stronger bond of trust, make them feel more engaged and foster an environment that allows for more effective decisions.

Related: 5 Tips for Managing Change in the Workplace

5. Reiterate the Goals and Purpose of the Decision

Throughout the decision-making process, it’s vital to avoid common management pitfalls and lose sight of the goals and purpose of the decision on the table.

The goals you’re working toward need to be clearly articulated at the outset of the decision-making process—and constantly reiterated throughout—to ensure they’re ultimately achieved.

“It’s easy, as you get into these conversations, to get so immersed in one substantive part of the equation that you lose track of what the actual purpose is,” Schlesinger says.

Revisiting purpose is especially important when making decisions related to complex initiatives—such as organizational change —to ensure your team feels motivated and aligned and understands how their contributions tie into larger objectives.

Why Are Decision-Making Skills Important?

Effective decision-making can immensely impact organizational performance. By developing your decision-making skills, you can exercise sound judgment and guide your team through the appropriate frameworks and processes—resulting in more data-driven decisions .

You can also anticipate and navigate organizational challenges while analyzing the outcomes of previous efforts, which can have lasting effects on your firm’s success.

Management Essentials | Get the job done | Learn More

Improve Your Decision-Making Skills

Enhancing your decision-making capabilities can be an integral part of your journey to becoming a better manager , reaching your business goals, and advancing your career. In addition to real-world experience, furthering your education by taking a management training course can equip you with a wide range of skills and knowledge that enable both your team and organization to thrive.

Do you want to design, direct, and shape organizational processes to your advantage? Explore Management Essentials , one of our online leadership and management courses , and discover how you can influence the context and environment in which decisions get made.

This post was updated on December 21, 2022. It was originally published on March 31, 2020.

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Critical Thinking

Decision-making and Problem-solving

Appreciate the complexities involved in decision-making & problem solving.

Develop evidence to support views

Analyze situations carefully

Discuss subjects in an organized way

Predict the consequences of actions

Weigh alternatives

Generate and organize ideas

Form and apply concepts

Design systematic plans of action

A 5-Step Problem-Solving Strategy

Specify the problem – a first step to solving a problem is to identify it as specifically as possible. It involves evaluating the present state and determining how it differs from the goal state.

Analyze the problem – analyzing the problem involves learning as much as you can about it. It may be necessary to look beyond the obvious, surface situation, to stretch your imagination and reach for more creative options.

seek other perspectives

be flexible in your analysis

consider various strands of impact

brainstorm about all possibilities and implications

research problems for which you lack complete information. Get help.

Formulate possible solutions – identify a wide range of possible solutions.

try to think of all possible solutions

be creative

consider similar problems and how you have solved them

Evaluate possible solutions – weigh the advantages and disadvantages of each solution. Think through each solution and consider how, when, and where you could accomplish each. Consider both immediate and long-term results. Mapping your solutions can be helpful at this stage.

Choose a solution – consider 3 factors:

compatibility with your priorities

amount of risk

practicality

Keys to Problem Solving

Think aloud – problem solving is a cognitive, mental process. Thinking aloud or talking yourself through the steps of problem solving is useful. Hearing yourself think can facilitate the process.

Allow time for ideas to "gel" or consolidate. If time permits, give yourself time for solutions to develop. Distance from a problem can allow you to clear your mind and get a new perspective.

Talk about the problem – describing the problem to someone else and talking about it can often make a problem become more clear and defined so that a new solution will surface.

Decision Making Strategies

Decision making is a process of identifying and evaluating choices. We make numerous decisions every day and our decisions may range from routine, every-day types of decisions to those decisions which will have far reaching impacts. The types of decisions we make are routine, impulsive, and reasoned. Deciding what to eat for breakfast is a routine decision; deciding to do or buy something at the last minute is considered an impulsive decision; and choosing your college major is, hopefully, a reasoned decision. College coursework often requires you to make the latter, or reasoned decisions.

Decision making has much in common with problem solving. In problem solving you identify and evaluate solution paths; in decision making you make a similar discovery and evaluation of alternatives. The crux of decision making, then, is the careful identification and evaluation of alternatives. As you weigh alternatives, use the following suggestions:

Consider the outcome each is likely to produce, in both the short term and the long term.

Compare alternatives based on how easily you can accomplish each.

Evaluate possible negative side effects each may produce.

Consider the risk involved in each.

Be creative, original; don't eliminate alternatives because you have not heard or used them before.

An important part of decision making is to predict both short-term and long-term outcomes for each alternative. You may find that while an alternative seems most desirable at the present, it may pose problems or complications over a longer time period.

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Understanding Science

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Teaching resources

Found 4 resources for the concept: Problem-solving and decision-making benefit from a scientific approach.

Grade Level(s):

UC Museum of Paleontology

Resource type:

lab activity

Discipline:

Life Science

Time: 10-15 minutes

This short activity quickly engages the participants in the process of developing testable hypotheses. Students come up with multiple hypotheses to explain a set of observations and figure out how to test these hypotheses.

View details »

Fair tests: A do-it-yourself guide

Science Story

Time: 15 minutes

Teach about test design in science using an example from everyday life: comparing chocolate chip cookie recipes. Get tips on using Science Stories in class .

Investigating a Crime Scene

powerpoint slides

Time: 30 minutes

Two suspicious dogs and a shredded book provide a perfect combination for focusing on the process of science and to do so with a bit of a chuckle. This powerpoint has been developed so that you can ask for student responses throughout.

Number patterns

classroom activity

Time: 15-20 minutes

Students try to discover the relationship among six numbers. The objective of this activity is to engage students in a problem-solving situation in which they practice aspects of the process of science.

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COMMENTS

Using the Scientific Method to Solve Problems
The scientific method is a process used to explore observations and answer questions. Originally used by scientists looking to prove new theories, its use has spread into many other areas, including that of problem-solving and decision-making. The scientific method is designed to eliminate the influences of bias, prejudice and personal beliefs ...
MS 3053 Review Flashcards
Study with Quizlet and memorize flashcards containing terms like Management science and operations research are _____, _____ uses scientific methodology in managerial decision making and problem solving., Management science uses _____ considerations in order to establish policies that are in the best interest of the organization. and more.
The Management Science Approach to Problem Solving
The results of the management science techniques in this text are examples of the two types shown in this section: (1) solutions/decisions and (2) descriptive results. Implementation . The final step in the management science process for problem solving described in Figure 1.1 is implementation.
What Is Management Science? + How to Enter This Field
Management science is the study of problem-solving and decision-making in organizations. You can think of it as applying the scientific method to management, enabling managers to make decisions for an organization and improve its performance. For example, health care facilities can use management science to determine the necessary information ...
A
6.1 Overview of Managerial Decision-Making; ... In other words, the scientific approach to problem-solving sets some fairly rigorous standards in an attempt to substitute objectivity for subjectivity. The scientific method in organizational behavior consists of four stages: (1) observation of the phenomena (facts) in the real world, (2 ...
Managerial Problem Solving and Decision Making
Decision-making can be further aided by the use of models. A decision-making model, also. called a problem-solving model, is based on logical steps. They are used to rationally analyze a. problem ...
What is Operations Research?
The simple answer: Operations Research (OR) is a discipline of problem-solving and decision-making that uses advanced analytical methods to help management run an effective organization. Problems are broken down, analyzed and solved in steps. Identify a problem. Build a model around the real-world problem. Use the model and data to arrive at ...
(PDF) Research Methods for Managerial decisions
Research Methods for Managerial decisions. July 2017. Publisher: LAP LAMBERT ACADEMIC PUBLISHING. ISBN: 978-3-659-76536-. Authors: Dr.Sridhar Seshadri.
Problem Solving and Decision Making
Decision making is the process of selecting and choosing one action or behavior out of several alternatives. Like problem solving, decision making involves the coordination of memories and executive resources. Research on decision making has paid particular attention to the cognitive biases that account for suboptimal decisions and decisions ...
Operations Research and Optimization Techniques
Operations research is, in principle, the application of scientific methods, techniques, and tools for solving problems involving the operations of a system in order to provide those in control of the system with optimum solutions to problems. Put simply, it is a systematic and analytical approach to decision making and problem solving.
A Guide to Using the Scientific Method in Everyday Life
The scientific method—the process used by scientists to understand the natural world—has the merit of investigating natural phenomena in a rigorous manner. Working from hypotheses, scientists draw conclusions based on empirical data. These data are validated on large-scale numbers and take into consideration the intrinsic variability of the real world.
Management science
management science, any application of science to the study of management. Originally a synonym for operations research, the term management science (often used in the plural) now designates a distinct field. Whereas operations research affords analytical data, statistics, and methods to increase the efficiency of management systems, management ...
The Scientific Method Steps, Uses, and Key Terms
When conducting research, the scientific method steps to follow are: Observe what you want to investigate. Ask a research question and make predictions. Test the hypothesis and collect data. Examine the results and draw conclusions. Report and share the results. This process not only allows scientists to investigate and understand different ...
5 Key Decision-Making Techniques for Managers
3. Foster a Collaborative Mindset. Fostering the right mindset early in the decision-making process is critical to ensuring your team works collaboratively—not contentiously. When facing a decision, there are two key mindsets to consider: Advocacy: A mindset that regards decision-making as a contest.
Decision-making and Problem-solving
Decision making is a process of identifying and evaluating choices. We make numerous decisions every day and our decisions may range from routine, every-day types of decisions to those decisions which will have far reaching impacts. The types of decisions we make are routine, impulsive, and reasoned. Deciding what to eat for breakfast is a ...
Managerial Decision Making
Decision trees. Jonathan P. Pinder, in Introduction to Business Analytics Using Simulation (Second Edition), 2022 Introduction. This chapter provides extensive confirmation of the need for using probability in managerial decision making.To accomplish this, a general decision-making process is defined along with the essential concepts of how to construct and use a decision tree to map out ...
Management Science Flashcards
Management science is the. application of a scientific approach to solving management problems in order to help managers make better decisions. ___ is a recognized and established discipline in business. Management Science. Management Science is also referred to. operations research, quantitative methods, quantitative analysis, and decision ...
Problem-solving and decision-making benefit from a scientific approach
Overview. Students try to discover the relationship among six numbers. The objective of this activity is to engage students in a problem-solving situation in which they practice aspects of the process of science. View details ».
Solved Define the terms management science and operations
Step 1. Management science applies mathematical modeling, statistical analysis, and optimization methods to ... Define the terms management science and operations research. Management science and operations research are terms that can be used interchangeably ---Select- uses scientific methodology in managerial decision making and problem solving.
Solved uses scientific methodology in managerial decision
Question: uses scientific methodology in managerial decision making and problem solving. uses scientific methodology in managerial decision making and problem solving. Here's the best way to solve it.
Management Science and Analytics (Quiz 1) Flashcards
The field of management science: a. is completely separate and distinct from all other disciplines. b. approaches decision making irrationally with techniques based on the scientific method. c. is another name for management or human resources management. d. concentrates on the use of quantitative methods to assist managers in decision making.
Solved Used scientific methodology in managerial decision
Question: Used scientific methodology in managerial decision making and problem solving Used scientific methodology in managerial decision making and problem solving Here's the best way to solve it.
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Operations Management questions and answers. QUESTION PARTS 1 2 3 TOTAL POINTS -/0.33 -/0.33 -10.34 -/1 SUBMISSIONS USED 0/5 0/5 0/5 Management science and operations research are? 1.Terms that can be used interchangeable 2. separate and distinct discipline What uses Scientific methodology in managerial decision making and problem solving? 1.