what chapter of research is hypothesis

What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By:  Derek Jansen (MBA)  | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing. 

Research Hypothesis 101

• What is a hypothesis ?
• What is a research hypothesis (scientific hypothesis)?
• Requirements for a research hypothesis
• Definition of a research hypothesis
• The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.  

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference. 

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell. 

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

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This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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16 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

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How to Develop a Good Research Hypothesis

The story of a research study begins by asking a question. Researchers all around the globe are asking curious questions and formulating research hypothesis. However, whether the research study provides an effective conclusion depends on how well one develops a good research hypothesis. Research hypothesis examples could help researchers get an idea as to how to write a good research hypothesis.

This blog will help you understand what is a research hypothesis, its characteristics and, how to formulate a research hypothesis

Table of Contents

What is Hypothesis?

Hypothesis is an assumption or an idea proposed for the sake of argument so that it can be tested. It is a precise, testable statement of what the researchers predict will be outcome of the study.  Hypothesis usually involves proposing a relationship between two variables: the independent variable (what the researchers change) and the dependent variable (what the research measures).

What is a Research Hypothesis?

Research hypothesis is a statement that introduces a research question and proposes an expected result. It is an integral part of the scientific method that forms the basis of scientific experiments. Therefore, you need to be careful and thorough when building your research hypothesis. A minor flaw in the construction of your hypothesis could have an adverse effect on your experiment. In research, there is a convention that the hypothesis is written in two forms, the null hypothesis, and the alternative hypothesis (called the experimental hypothesis when the method of investigation is an experiment).

Characteristics of a Good Research Hypothesis

As the hypothesis is specific, there is a testable prediction about what you expect to happen in a study. You may consider drawing hypothesis from previously published research based on the theory.

A good research hypothesis involves more effort than just a guess. In particular, your hypothesis may begin with a question that could be further explored through background research.

To help you formulate a promising research hypothesis, you should ask yourself the following questions:

• Is the language clear and focused?
• What is the relationship between your hypothesis and your research topic?
• Is your hypothesis testable? If yes, then how?
• What are the possible explanations that you might want to explore?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate your variables without hampering the ethical standards?
• Does your research predict the relationship and outcome?
• Is your research simple and concise (avoids wordiness)?
• Is it clear with no ambiguity or assumptions about the readers’ knowledge
• Is your research observable and testable results?
• Is it relevant and specific to the research question or problem?

The questions listed above can be used as a checklist to make sure your hypothesis is based on a solid foundation. Furthermore, it can help you identify weaknesses in your hypothesis and revise it if necessary.

Source: Educational Hub

How to formulate a research hypothesis.

A testable hypothesis is not a simple statement. It is rather an intricate statement that needs to offer a clear introduction to a scientific experiment, its intentions, and the possible outcomes. However, there are some important things to consider when building a compelling hypothesis.

1. State the problem that you are trying to solve.

Make sure that the hypothesis clearly defines the topic and the focus of the experiment.

2. Try to write the hypothesis as an if-then statement.

Follow this template: If a specific action is taken, then a certain outcome is expected.

3. Define the variables

Independent variables are the ones that are manipulated, controlled, or changed. Independent variables are isolated from other factors of the study.

Dependent variables , as the name suggests are dependent on other factors of the study. They are influenced by the change in independent variable.

4. Scrutinize the hypothesis

Evaluate assumptions, predictions, and evidence rigorously to refine your understanding.

Types of Research Hypothesis

The types of research hypothesis are stated below:

1. Simple Hypothesis

It predicts the relationship between a single dependent variable and a single independent variable.

2. Complex Hypothesis

It predicts the relationship between two or more independent and dependent variables.

3. Directional Hypothesis

It specifies the expected direction to be followed to determine the relationship between variables and is derived from theory. Furthermore, it implies the researcher’s intellectual commitment to a particular outcome.

4. Non-directional Hypothesis

It does not predict the exact direction or nature of the relationship between the two variables. The non-directional hypothesis is used when there is no theory involved or when findings contradict previous research.

5. Associative and Causal Hypothesis

The associative hypothesis defines interdependency between variables. A change in one variable results in the change of the other variable. On the other hand, the causal hypothesis proposes an effect on the dependent due to manipulation of the independent variable.

6. Null Hypothesis

Null hypothesis states a negative statement to support the researcher’s findings that there is no relationship between two variables. There will be no changes in the dependent variable due the manipulation of the independent variable. Furthermore, it states results are due to chance and are not significant in terms of supporting the idea being investigated.

7. Alternative Hypothesis

It states that there is a relationship between the two variables of the study and that the results are significant to the research topic. An experimental hypothesis predicts what changes will take place in the dependent variable when the independent variable is manipulated. Also, it states that the results are not due to chance and that they are significant in terms of supporting the theory being investigated.

Research Hypothesis Examples of Independent and Dependent Variables

Research Hypothesis Example 1 The greater number of coal plants in a region (independent variable) increases water pollution (dependent variable). If you change the independent variable (building more coal factories), it will change the dependent variable (amount of water pollution).

Research Hypothesis Example 2 What is the effect of diet or regular soda (independent variable) on blood sugar levels (dependent variable)? If you change the independent variable (the type of soda you consume), it will change the dependent variable (blood sugar levels)

You should not ignore the importance of the above steps. The validity of your experiment and its results rely on a robust testable hypothesis. Developing a strong testable hypothesis has few advantages, it compels us to think intensely and specifically about the outcomes of a study. Consequently, it enables us to understand the implication of the question and the different variables involved in the study. Furthermore, it helps us to make precise predictions based on prior research. Hence, forming a hypothesis would be of great value to the research. Here are some good examples of testable hypotheses.

More importantly, you need to build a robust testable research hypothesis for your scientific experiments. A testable hypothesis is a hypothesis that can be proved or disproved as a result of experimentation.

Importance of a Testable Hypothesis

To devise and perform an experiment using scientific method, you need to make sure that your hypothesis is testable. To be considered testable, some essential criteria must be met:

• There must be a possibility to prove that the hypothesis is true.
• There must be a possibility to prove that the hypothesis is false.
• The results of the hypothesis must be reproducible.

Without these criteria, the hypothesis and the results will be vague. As a result, the experiment will not prove or disprove anything significant.

What are your experiences with building hypotheses for scientific experiments? What challenges did you face? How did you overcome these challenges? Please share your thoughts with us in the comments section.

Frequently Asked Questions

The steps to write a research hypothesis are: 1. Stating the problem: Ensure that the hypothesis defines the research problem 2. Writing a hypothesis as an 'if-then' statement: Include the action and the expected outcome of your study by following a ‘if-then’ structure. 3. Defining the variables: Define the variables as Dependent or Independent based on their dependency to other factors. 4. Scrutinizing the hypothesis: Identify the type of your hypothesis

Hypothesis testing is a statistical tool which is used to make inferences about a population data to draw conclusions for a particular hypothesis.

Hypothesis in statistics is a formal statement about the nature of a population within a structured framework of a statistical model. It is used to test an existing hypothesis by studying a population.

Research hypothesis is a statement that introduces a research question and proposes an expected result. It forms the basis of scientific experiments.

The different types of hypothesis in research are: • Null hypothesis: Null hypothesis is a negative statement to support the researcher’s findings that there is no relationship between two variables. • Alternate hypothesis: Alternate hypothesis predicts the relationship between the two variables of the study. • Directional hypothesis: Directional hypothesis specifies the expected direction to be followed to determine the relationship between variables. • Non-directional hypothesis: Non-directional hypothesis does not predict the exact direction or nature of the relationship between the two variables. • Simple hypothesis: Simple hypothesis predicts the relationship between a single dependent variable and a single independent variable. • Complex hypothesis: Complex hypothesis predicts the relationship between two or more independent and dependent variables. • Associative and casual hypothesis: Associative and casual hypothesis predicts the relationship between two or more independent and dependent variables. • Empirical hypothesis: Empirical hypothesis can be tested via experiments and observation. • Statistical hypothesis: A statistical hypothesis utilizes statistical models to draw conclusions about broader populations.

Wow! You really simplified your explanation that even dummies would find it easy to comprehend. Thank you so much.

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I enjoy reading the post. Hypotheses are actually an intrinsic part in a study. It bridges the research question and the methodology of the study.

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It very interesting to read the topic, can you guide me any specific example of hypothesis process establish throw the Demand and supply of the specific product in market

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It is really a useful for me Kindly give some examples of hypothesis

It was a well explained content ,can you please give me an example with the null and alternative hypothesis illustrated

clear and concise. thanks.

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What is and How to Write a Good Hypothesis in Research?

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Table of Contents

One of the most important aspects of conducting research is constructing a strong hypothesis. But what makes a hypothesis in research effective? In this article, we’ll look at the difference between a hypothesis and a research question, as well as the elements of a good hypothesis in research. We’ll also include some examples of effective hypotheses, and what pitfalls to avoid.

What is a Hypothesis in Research?

Simply put, a hypothesis is a research question that also includes the predicted or expected result of the research. Without a hypothesis, there can be no basis for a scientific or research experiment. As such, it is critical that you carefully construct your hypothesis by being deliberate and thorough, even before you set pen to paper. Unless your hypothesis is clearly and carefully constructed, any flaw can have an adverse, and even grave, effect on the quality of your experiment and its subsequent results.

Research Question vs Hypothesis

It’s easy to confuse research questions with hypotheses, and vice versa. While they’re both critical to the Scientific Method, they have very specific differences. Primarily, a research question, just like a hypothesis, is focused and concise. But a hypothesis includes a prediction based on the proposed research, and is designed to forecast the relationship of and between two (or more) variables. Research questions are open-ended, and invite debate and discussion, while hypotheses are closed, e.g. “The relationship between A and B will be C.”

A hypothesis is generally used if your research topic is fairly well established, and you are relatively certain about the relationship between the variables that will be presented in your research. Since a hypothesis is ideally suited for experimental studies, it will, by its very existence, affect the design of your experiment. The research question is typically used for new topics that have not yet been researched extensively. Here, the relationship between different variables is less known. There is no prediction made, but there may be variables explored. The research question can be casual in nature, simply trying to understand if a relationship even exists, descriptive or comparative.

How to Write Hypothesis in Research

Writing an effective hypothesis starts before you even begin to type. Like any task, preparation is key, so you start first by conducting research yourself, and reading all you can about the topic that you plan to research. From there, you’ll gain the knowledge you need to understand where your focus within the topic will lie.

Remember that a hypothesis is a prediction of the relationship that exists between two or more variables. Your job is to write a hypothesis, and design the research, to “prove” whether or not your prediction is correct. A common pitfall is to use judgments that are subjective and inappropriate for the construction of a hypothesis. It’s important to keep the focus and language of your hypothesis objective.

An effective hypothesis in research is clearly and concisely written, and any terms or definitions clarified and defined. Specific language must also be used to avoid any generalities or assumptions.

Use the following points as a checklist to evaluate the effectiveness of your research hypothesis:

• Predicts the relationship and outcome
• Simple and concise – avoid wordiness
• Clear with no ambiguity or assumptions about the readers’ knowledge
• Observable and testable results
• Relevant and specific to the research question or problem

Research Hypothesis Example

Perhaps the best way to evaluate whether or not your hypothesis is effective is to compare it to those of your colleagues in the field. There is no need to reinvent the wheel when it comes to writing a powerful research hypothesis. As you’re reading and preparing your hypothesis, you’ll also read other hypotheses. These can help guide you on what works, and what doesn’t, when it comes to writing a strong research hypothesis.

Here are a few generic examples to get you started.

Eating an apple each day, after the age of 60, will result in a reduction of frequency of physician visits.

Budget airlines are more likely to receive more customer complaints. A budget airline is defined as an airline that offers lower fares and fewer amenities than a traditional full-service airline. (Note that the term “budget airline” is included in the hypothesis.

Workplaces that offer flexible working hours report higher levels of employee job satisfaction than workplaces with fixed hours.

Each of the above examples are specific, observable and measurable, and the statement of prediction can be verified or shown to be false by utilizing standard experimental practices. It should be noted, however, that often your hypothesis will change as your research progresses.

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Developing a Hypothesis

Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton

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.

Developing a Hypothesis Copyright © by Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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A concise guide to reproducible research using secondary data

Chapter 2 formulating a hypothesis.

“There is no single best way to develop a research idea.” ( Pischke 2012 )

2.1 How do you develop a research question and formulate a hypothesis?

You decide to undertake a scientific project. Where do you start? First, you need to find a research question that interests you and formulate a hypothesis. We will introduce some key terminology, steps you can take, and examples how to develop research questions. Note that .

What if someone assigns a topic to me? For students attending undergraduate and graduate courses that often pick topics from a list, all of these steps are equally important and necessary. You still need to formulate a research question and a hypothesis. And it is important to clarify the relevance of your topic for yourself.

When thinking about a research question, you need to identify a topic that is:

• Relevant , important in the world and interesting to you as a researcher: Does working on the topic excites you? You will spend many hours thinking about it and working on it. Therefore, it should be interesting and engaging enough for you to motivate your continued work on this topic.
• Specific : not too broad and not too narrow
• Feasible to research within a given time frame: Is it possible to answer the research question based on your time budget, data and additional resources.

How do you find a topic or develop a feasible research idea in the first place? Finding an idea is not difficult, the critical part is to find a good idea. How do you do that? There is no one specific way how one gets an idea, rather there is a myriad of ways how people come up with potential ideas (for example, as stated by Varian ( 2016 ) ).

You can find inspiration by

• Looking at insights from the world around you: your own life and experiences, observe the behavior of people around you
• Talking to people around you, experts, other students, family members
• Talking to individuals outside your field (non-economists)
• Talking to professionals working in the area you are interested in (you may use social media and professional platforms like LinkedIN or Twitter to make contact)
• Reading journal articles from other non-economic social sciences and the medical literature
• What are the issues being discussed?
• How do these issues affect people’s lives?

In addition you could

• Go to virtual and in-person seminars, for example, the Essen Health Economics Seminar
• Look at abstracts of scientific articles and working papers
• Look at the literature in a specific field you are interested in, for example, screening complete issues of journals or editorials about certain research advancements. By reading this literature you might come up with the idea on how to extend and refine previous research.

Once you identified a research question that is of interest to you, you need to define a hypothesis.

2.2 What is a hypothesis?

A hypothesis is a statement that introduces your research question and suggests the results you might find. It is an educated guess. You start by posing an economic question and formulate a hypothesis about this question. Then you test it with your data and empirical analysis and either accept or reject the hypothesis. It constitutes the main basis of your scientific investigation and you should be careful when creating it.

2.2.1 Develop a hypothesis

Before you formulate your hypothesis, read up on the topic of interest. This should provide you with sufficient information to narrow down your research question. Once you find your question you need to develop a hypothesis, which contains a statement of your expectations regarding your research question’s results. You propose to prove your hypothesis with your research by testing the relationship between two variables of interest. Thus, a hypothesis should be testable with the data at hand. There are two types of hypotheses: alternative or null. Null states that there is no effect. Alternative states that there is an effect.

There is an alternative view on this that suggests one should not look at the literature too early on in the idea-generating process to not be influenced and shaped by someone else’s ideas ( Varian 2016 ) . According to this view you can spend some time (i.e. a few weeks) trying to develop your own original idea. Even if you end up with an idea that has already been pursued by someone else, this will still provide you with good practice in developing publishable ideas. After you have developed an idea and made sure that it was not yet investigated in the literature, you can start conducting a systematic literature review. By doing this, you can find some other interesting insights from the work of others that you can synthesize in your own work to produce something novel and original.

2.2.2 Identify relevant literature

For your research project you will need to identify and collect previous relevant literature. It should involve a thorough search of the keywords in relevant databases and journals. Place emphasis on articles from high-ranking journals with significant numbers of citations. This will give you an indication of the most influential and important work in the field. Once you identify and collect the relevant literature for your topic, you will need to critically synthesize it in your literature review.

When you perform your literature review, consider theories that may inform your research question. For example, when studying physician behavior you may consider principal-agent theory.

2.2.3 Research question or literature review: the chicken or the egg problem?

Whether you start reading the literature first or by developing an idea may depend on your level (graduate student, early career researcher) and other goals. However, thinking freely about what you like to investigate first may help to critically develop a feasible and interesting research question.

We highlight an example how to start with investigating the real world and subsequently posing a research question ( “How to Write a Strong Hypothesis Steps and Examples ” 2019 ; “Developing Strong Research Questions Criteria and Examples ” 2019 ; Schilbach 2019 ) . For example, based on your observation you notice that people spend extensive amount of time looking at their smartphones. Maybe even you yourself engage in the same behavior. In addition, you read a BBC News article Social media damages teenagers’ mental health, report says .

(#fig:social_media)Social media and mental health

Source: BBC

You decide to translate this article and your observations into a research question : How does social media use affect mental health? Before you formulate your hypothesis, read up on the topic of interest. Read economic, medical and other social science literature on the topic. There is likely to be a vast amount of literature from non-economic fields that are doing research on your topic of interest, for example, psychology or neuroscience. Familiarize yourself with it and master it. Do not get distracted by different scientific methodologies and techniques that might seem not up-to-par to the economic studies (small sample sizes, endogeneity, uncovering association rather than causation, etc.), but rather focus on suggestions of potential mechanisms.

A hypothesis is then your research question distilled into a one sentence statement, which presents your expectations regarding the results. You propose to prove your hypothesis by testing the relationship between two variables of interest with the data at hand. There are two types of hypotheses: alternative or null. The null hypothesis states that there is no effect. The alternative hypothesis states that there is an effect.

A hypothesis related to the above-stated research question could be: The increased use of social media among teenagers leads to (is associated with) worse mental health outcomes, i.e. increased incidence of depression, eating disorders, worse well-being and lower self-esteem. It suggests a direction of a relationship that you expect to find that is guided by your observations and existing evidence. It is testable with scientific research methods by using statistical analysis of the relevant data.

Your hypothesis suggests a relationship between two variables: social media use (your independent variable \(X\) ) and mental health (dependent variable \(Y\) ). It could be framed in terms of correlation (is associated with) or causation (leads to). This should be reflected in the choice of scientific investigation you decide to undertake.

The null hypothesis is: There is no relationship between social media use among teenagers and their mental health .

2.3 Resources box

2.3.1 how to develop strong research questions.

• The form of the research process
• Varian, H. R. (2016). How to build an economic model in your spare time. The American Economist, 61(1), 81-90.

2.3.2 Identify relevant literature from major general interest and field literature

To identify the relevant literature you can

• use academic search engines such as Google Scholar, Web of Science, EconLit, PubMed.
• search working paper series such as the National Bureau of Economic Research , NetEc or IZA
• search more general resource sites such as Resources for Economists
• go to the library/use library database

2.3.3 Assess the quality of a journal article

Several rankings may help to assess the quality of research you consider

• Journals of general interest and by field in economics and management - For German-speaking countries, consider the VWL / BWL Handelsblatt Ranking for economics and management - The German Association of Management Scholars provides an expert-based ranking VHB JourQual 3.0, Teilranking Management im Gesundheitswesen - Web of Science Impact Factors - Scimago
• Health Economics, Health Services and Health Care Managment Research: Health Economics Journals List
• Be aware that like in any other domain there are predatory publishing practices .

Use tools to investigate how a journal article is connected to other works

• Citationgecko
• Connected papers
• scite_ – a tool to get a first impression whether a study is disputed or academic consensus

2.3.4 Organize your literature

• Zotero (free of charge)
• Mendeley (free of charge)
• EndNote (potentially free of charge via your university)
• Citavi (potentially free of charge via your university)
• BibTEX if you work with TEX
• Excel spread sheet

2.4 Checklist to get started with formulating your hypothesis

• Find an interesting and relevant research topic, if not assigned
• Try to suck up all information you can easily obtain from various sources within and outside academic literature
• Formulate one compelling research question
• Find the best available empirical and theoretical evidence that is related to your research question
• Formulate a hypothesis
• Check whether data are available for analysis
• Challenge your idea with your fellows or senior researchers

2.5 Example: Hellerstein ( 1998 )

As an illustration of the research process of formulating a hypothesis, designing a study, running a study, collecting and analyzing the data and, finally, reporting the study, we provide an example by replicating Judith K. Hellerstein’s paper “The Importance of the Physician in the Generic versus Trade-Name Prescription Decision” that was published in 1998 in the RAND Journal of Economics.

Hellerstein’s 1998 paper has impacted discussion about behavioral factors of physician decisions and pharmaceutical markets over two decades. The study received 448 citations on Google Scholar since 1998 by 27/03/2022, including recent mentions in top field journals such as Journal of Public Economics (2021) , Journal of Health Economics (2019) , and Health Economics (2019) .

Figure 2.1: Connected graph of Hellerstein ( 1998 ) , February 2022

Figure 2.1 shows a connected graph of prior and derivative works related to the study.

The work has impacted the literature researching the role of physician behavior and its influence on access, adoption and diffusion of health services, moral hazard and incentives in prescription and treatment decisions and the influence of different payment schemes, and a vast body of literature studying the pharmaceutical market.

The research that has been influenced by Hellerstein includes evidence on:

• generic drug entries and market efficiency
• the effectiveness of pharmaceutical promotion
• the effectiveness of price regulations
• the role of patents and dynamics of market segmentation

At the end of each chapter, we demonstrate insights into this study that we replicate.

2.5.1 Context of the study - escalating health expenditures

In the United States, the total prescription drug expenditure in 2020 marked about 358.7 billion US Dollars ( Statista n.d. ) . The prescription of generic drugs in comparison to more expensive brand-name versions is an option in reducing the total health care expenditure. Generic drugs are bioequivalent in the active ingredients and can serve as a channel to contain prescription expenditure ( Kesselheim 2008 ) as generic drugs are between 20 and 90% cheaper than their trade-name alternatives ( Dunne et al. 2013 ) .

2.5.2 Research question - How does a patient’s insurance status influence the physician’s choice between generic compared to brand-name drugs?

Physicians are faced with a multitude of medication options, including the choice between generic and trade-name drugs. Physicians ideally act as agents for their patients to identify the best available treatment option based on their needs. Choosing the best treatment entails cost of coordination and cognition. The prescription of generic drugs may serve as an example to what extent physicians customize treatments according to patients’ needs with regards to cost. From an economic point of view we may expect that once a generic drug is available, a perfectly rational agent (i.e. physician) would prescribe a generic drug instead of the trade-name version if therapeutically identical ( Dranove 1989 ) . This leads to the following research question: “Do physicians vary their prescription decisions on a patient-by-patient basis or do they systematically prescribe the same version, trade-name or generic, to all patients?” .

The 1998 Hellerstein’s study examines two hypotheses:

• The physician prescribing choice influences the selection of a generic over a brand-name drug
• The patient’s insurance status influences the physician’s choice between generic and brand-name drugs.

For the purpose of this example and in the replication exercise we focus on the second aspect.

2.5.3 Hypothesis

The paper formulates the following hypothesis:

Physicians are more likely to prescribe generics to patients who do not have insurance coverage for prescription pharmaceuticals (moral hazard in insurance)

Hellerstein ( 1998 ) discusses that, based on insurance status, some patients may demand certain care more than others. If, for example, the prescription drug is reimbursed by the patient’s health insurance, this may cause overconsumption. This behavior can potentially differ by the patient’s insurance scheme. A patient that has no insurance and, thus, does not get any reimbursement for prescription drugs, might have a higher incentive to demand cheaper generic drugs ( Danzon and Furukawa 2011 ) than a patient with insurance that covers prescription drugs, either generic or trade-name. Given that the United States have different insurance schemes with varying prescription drug coverage, it is of interest to investigate the role of a patient’s insurance status in the physician’s choice between generic compared to brand-name drugs.

Hellerstein ( 1998 ) considers a patient’s insurance status as a matter of dividing the study population in groups for which the choice between generic and brand-name drugs differs. She suggests that There is a relationship between the prescription of a generic drug and insurance status of a patient. ( Hellerstein 1998 ) .

Providing answers to a research question requires formulating and testing a hypothesis. Based on logic, theory or previous research, a hypothesis proposes an expected relationship within the given data. According to her research question, Hellerstein hypothesizes that: Physicians are more likely to prescribe generics to patients who do not have insurance coverage for prescription pharmaceuticals.

Specifically, she writes “if there is moral hazard in insurance when it comes to physician prescription behavior, there will be differences in the propensity of physicians to prescribe low-cost generic drugs, and these differences will be (partially) a function of the insurance held by the patient. In particular, if moral hazard exists, patients with extensive insurance coverage for prescription drugs (like those on Medicaid in 1989) should receive prescriptions written for generic drugs less frequently than patients with no prescription drug coverage.” ( Hellerstein 1998, 113 )

Based on Hellerstein’s considerations, we expect the effect of the insurance status on whether a patient receives a generic to be different from zero. To obtain a testable null hypothesis, we reformulate this relationship so that we reject the hypothesis if our expectations are correct. This means, if we expect to see an effect of insurance on prescriptions of generics, our null hypothesis is that insurance status has no effect on the outcome (prescription of generic drugs). No moral hazard arises from having obtained insurance.

2.4 Developing a Hypothesis

Learning objectives.

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

Theories and Hypotheses

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

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

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

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

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

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

Theory Testing

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

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

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

Incorporating Theory into Your Research

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

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

Characteristics of a Good Hypothesis

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

Key Takeaways

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

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• v.53(4); 2010 Aug

Research questions, hypotheses and objectives

Patricia farrugia.

* Michael G. DeGroote School of Medicine, the

Bradley A. Petrisor

† Division of Orthopaedic Surgery and the

Forough Farrokhyar

‡ Departments of Surgery and

§ Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ont

Mohit Bhandari

There is an increasing familiarity with the principles of evidence-based medicine in the surgical community. As surgeons become more aware of the hierarchy of evidence, grades of recommendations and the principles of critical appraisal, they develop an increasing familiarity with research design. Surgeons and clinicians are looking more and more to the literature and clinical trials to guide their practice; as such, it is becoming a responsibility of the clinical research community to attempt to answer questions that are not only well thought out but also clinically relevant. The development of the research question, including a supportive hypothesis and objectives, is a necessary key step in producing clinically relevant results to be used in evidence-based practice. A well-defined and specific research question is more likely to help guide us in making decisions about study design and population and subsequently what data will be collected and analyzed. 1

Objectives of this article

In this article, we discuss important considerations in the development of a research question and hypothesis and in defining objectives for research. By the end of this article, the reader will be able to appreciate the significance of constructing a good research question and developing hypotheses and research objectives for the successful design of a research study. The following article is divided into 3 sections: research question, research hypothesis and research objectives.

Research question

Interest in a particular topic usually begins the research process, but it is the familiarity with the subject that helps define an appropriate research question for a study. 1 Questions then arise out of a perceived knowledge deficit within a subject area or field of study. 2 Indeed, Haynes suggests that it is important to know “where the boundary between current knowledge and ignorance lies.” 1 The challenge in developing an appropriate research question is in determining which clinical uncertainties could or should be studied and also rationalizing the need for their investigation.

Increasing one’s knowledge about the subject of interest can be accomplished in many ways. Appropriate methods include systematically searching the literature, in-depth interviews and focus groups with patients (and proxies) and interviews with experts in the field. In addition, awareness of current trends and technological advances can assist with the development of research questions. 2 It is imperative to understand what has been studied about a topic to date in order to further the knowledge that has been previously gathered on a topic. Indeed, some granting institutions (e.g., Canadian Institute for Health Research) encourage applicants to conduct a systematic review of the available evidence if a recent review does not already exist and preferably a pilot or feasibility study before applying for a grant for a full trial.

In-depth knowledge about a subject may generate a number of questions. It then becomes necessary to ask whether these questions can be answered through one study or if more than one study needed. 1 Additional research questions can be developed, but several basic principles should be taken into consideration. 1 All questions, primary and secondary, should be developed at the beginning and planning stages of a study. Any additional questions should never compromise the primary question because it is the primary research question that forms the basis of the hypothesis and study objectives. It must be kept in mind that within the scope of one study, the presence of a number of research questions will affect and potentially increase the complexity of both the study design and subsequent statistical analyses, not to mention the actual feasibility of answering every question. 1 A sensible strategy is to establish a single primary research question around which to focus the study plan. 3 In a study, the primary research question should be clearly stated at the end of the introduction of the grant proposal, and it usually specifies the population to be studied, the intervention to be implemented and other circumstantial factors. 4

Hulley and colleagues 2 have suggested the use of the FINER criteria in the development of a good research question ( Box 1 ). The FINER criteria highlight useful points that may increase the chances of developing a successful research project. A good research question should specify the population of interest, be of interest to the scientific community and potentially to the public, have clinical relevance and further current knowledge in the field (and of course be compliant with the standards of ethical boards and national research standards).

FINER criteria for a good research question

Adapted with permission from Wolters Kluwer Health. 2

Whereas the FINER criteria outline the important aspects of the question in general, a useful format to use in the development of a specific research question is the PICO format — consider the population (P) of interest, the intervention (I) being studied, the comparison (C) group (or to what is the intervention being compared) and the outcome of interest (O). 3 , 5 , 6 Often timing (T) is added to PICO ( Box 2 ) — that is, “Over what time frame will the study take place?” 1 The PICOT approach helps generate a question that aids in constructing the framework of the study and subsequently in protocol development by alluding to the inclusion and exclusion criteria and identifying the groups of patients to be included. Knowing the specific population of interest, intervention (and comparator) and outcome of interest may also help the researcher identify an appropriate outcome measurement tool. 7 The more defined the population of interest, and thus the more stringent the inclusion and exclusion criteria, the greater the effect on the interpretation and subsequent applicability and generalizability of the research findings. 1 , 2 A restricted study population (and exclusion criteria) may limit bias and increase the internal validity of the study; however, this approach will limit external validity of the study and, thus, the generalizability of the findings to the practical clinical setting. Conversely, a broadly defined study population and inclusion criteria may be representative of practical clinical practice but may increase bias and reduce the internal validity of the study.

PICOT criteria 1

A poorly devised research question may affect the choice of study design, potentially lead to futile situations and, thus, hamper the chance of determining anything of clinical significance, which will then affect the potential for publication. Without devoting appropriate resources to developing the research question, the quality of the study and subsequent results may be compromised. During the initial stages of any research study, it is therefore imperative to formulate a research question that is both clinically relevant and answerable.

Research hypothesis

The primary research question should be driven by the hypothesis rather than the data. 1 , 2 That is, the research question and hypothesis should be developed before the start of the study. This sounds intuitive; however, if we take, for example, a database of information, it is potentially possible to perform multiple statistical comparisons of groups within the database to find a statistically significant association. This could then lead one to work backward from the data and develop the “question.” This is counterintuitive to the process because the question is asked specifically to then find the answer, thus collecting data along the way (i.e., in a prospective manner). Multiple statistical testing of associations from data previously collected could potentially lead to spuriously positive findings of association through chance alone. 2 Therefore, a good hypothesis must be based on a good research question at the start of a trial and, indeed, drive data collection for the study.

The research or clinical hypothesis is developed from the research question and then the main elements of the study — sampling strategy, intervention (if applicable), comparison and outcome variables — are summarized in a form that establishes the basis for testing, statistical and ultimately clinical significance. 3 For example, in a research study comparing computer-assisted acetabular component insertion versus freehand acetabular component placement in patients in need of total hip arthroplasty, the experimental group would be computer-assisted insertion and the control/conventional group would be free-hand placement. The investigative team would first state a research hypothesis. This could be expressed as a single outcome (e.g., computer-assisted acetabular component placement leads to improved functional outcome) or potentially as a complex/composite outcome; that is, more than one outcome (e.g., computer-assisted acetabular component placement leads to both improved radiographic cup placement and improved functional outcome).

However, when formally testing statistical significance, the hypothesis should be stated as a “null” hypothesis. 2 The purpose of hypothesis testing is to make an inference about the population of interest on the basis of a random sample taken from that population. The null hypothesis for the preceding research hypothesis then would be that there is no difference in mean functional outcome between the computer-assisted insertion and free-hand placement techniques. After forming the null hypothesis, the researchers would form an alternate hypothesis stating the nature of the difference, if it should appear. The alternate hypothesis would be that there is a difference in mean functional outcome between these techniques. At the end of the study, the null hypothesis is then tested statistically. If the findings of the study are not statistically significant (i.e., there is no difference in functional outcome between the groups in a statistical sense), we cannot reject the null hypothesis, whereas if the findings were significant, we can reject the null hypothesis and accept the alternate hypothesis (i.e., there is a difference in mean functional outcome between the study groups), errors in testing notwithstanding. In other words, hypothesis testing confirms or refutes the statement that the observed findings did not occur by chance alone but rather occurred because there was a true difference in outcomes between these surgical procedures. The concept of statistical hypothesis testing is complex, and the details are beyond the scope of this article.

Another important concept inherent in hypothesis testing is whether the hypotheses will be 1-sided or 2-sided. A 2-sided hypothesis states that there is a difference between the experimental group and the control group, but it does not specify in advance the expected direction of the difference. For example, we asked whether there is there an improvement in outcomes with computer-assisted surgery or whether the outcomes worse with computer-assisted surgery. We presented a 2-sided test in the above example because we did not specify the direction of the difference. A 1-sided hypothesis states a specific direction (e.g., there is an improvement in outcomes with computer-assisted surgery). A 2-sided hypothesis should be used unless there is a good justification for using a 1-sided hypothesis. As Bland and Atlman 8 stated, “One-sided hypothesis testing should never be used as a device to make a conventionally nonsignificant difference significant.”

The research hypothesis should be stated at the beginning of the study to guide the objectives for research. Whereas the investigators may state the hypothesis as being 1-sided (there is an improvement with treatment), the study and investigators must adhere to the concept of clinical equipoise. According to this principle, a clinical (or surgical) trial is ethical only if the expert community is uncertain about the relative therapeutic merits of the experimental and control groups being evaluated. 9 It means there must exist an honest and professional disagreement among expert clinicians about the preferred treatment. 9

Designing a research hypothesis is supported by a good research question and will influence the type of research design for the study. Acting on the principles of appropriate hypothesis development, the study can then confidently proceed to the development of the research objective.

Research objective

The primary objective should be coupled with the hypothesis of the study. Study objectives define the specific aims of the study and should be clearly stated in the introduction of the research protocol. 7 From our previous example and using the investigative hypothesis that there is a difference in functional outcomes between computer-assisted acetabular component placement and free-hand placement, the primary objective can be stated as follows: this study will compare the functional outcomes of computer-assisted acetabular component insertion versus free-hand placement in patients undergoing total hip arthroplasty. Note that the study objective is an active statement about how the study is going to answer the specific research question. Objectives can (and often do) state exactly which outcome measures are going to be used within their statements. They are important because they not only help guide the development of the protocol and design of study but also play a role in sample size calculations and determining the power of the study. 7 These concepts will be discussed in other articles in this series.

From the surgeon’s point of view, it is important for the study objectives to be focused on outcomes that are important to patients and clinically relevant. For example, the most methodologically sound randomized controlled trial comparing 2 techniques of distal radial fixation would have little or no clinical impact if the primary objective was to determine the effect of treatment A as compared to treatment B on intraoperative fluoroscopy time. However, if the objective was to determine the effect of treatment A as compared to treatment B on patient functional outcome at 1 year, this would have a much more significant impact on clinical decision-making. Second, more meaningful surgeon–patient discussions could ensue, incorporating patient values and preferences with the results from this study. 6 , 7 It is the precise objective and what the investigator is trying to measure that is of clinical relevance in the practical setting.

The following is an example from the literature about the relation between the research question, hypothesis and study objectives:

Study: Warden SJ, Metcalf BR, Kiss ZS, et al. Low-intensity pulsed ultrasound for chronic patellar tendinopathy: a randomized, double-blind, placebo-controlled trial. Rheumatology 2008;47:467–71.

Research question: How does low-intensity pulsed ultrasound (LIPUS) compare with a placebo device in managing the symptoms of skeletally mature patients with patellar tendinopathy?

Research hypothesis: Pain levels are reduced in patients who receive daily active-LIPUS (treatment) for 12 weeks compared with individuals who receive inactive-LIPUS (placebo).

Objective: To investigate the clinical efficacy of LIPUS in the management of patellar tendinopathy symptoms.

The development of the research question is the most important aspect of a research project. A research project can fail if the objectives and hypothesis are poorly focused and underdeveloped. Useful tips for surgical researchers are provided in Box 3 . Designing and developing an appropriate and relevant research question, hypothesis and objectives can be a difficult task. The critical appraisal of the research question used in a study is vital to the application of the findings to clinical practice. Focusing resources, time and dedication to these 3 very important tasks will help to guide a successful research project, influence interpretation of the results and affect future publication efforts.

Tips for developing research questions, hypotheses and objectives for research studies

• Perform a systematic literature review (if one has not been done) to increase knowledge and familiarity with the topic and to assist with research development.
• Learn about current trends and technological advances on the topic.
• Seek careful input from experts, mentors, colleagues and collaborators to refine your research question as this will aid in developing the research question and guide the research study.
• Use the FINER criteria in the development of the research question.
• Ensure that the research question follows PICOT format.
• Develop a research hypothesis from the research question.
• Develop clear and well-defined primary and secondary (if needed) objectives.
• Ensure that the research question and objectives are answerable, feasible and clinically relevant.

FINER = feasible, interesting, novel, ethical, relevant; PICOT = population (patients), intervention (for intervention studies only), comparison group, outcome of interest, time.

Competing interests: No funding was received in preparation of this paper. Dr. Bhandari was funded, in part, by a Canada Research Chair, McMaster University.

Research and Hypothesis Testing: Moving from Theory to Experiment

• First Online: 14 November 2019

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• Aaron W. Calhoun 7 &
• Joshua Hui 8  

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In this chapter, we discuss the theoretical foundation for research and why theory is important for conducting experiments. We begin with a brief discussion of theory and its role in research. Next, we address the relationship between theory and hypotheses and distinguish between research questions and hypotheses. We then discuss theoretical constructs and how operational definitions make the constructs measurable. Next, we address the experiment and its role in establishing a plan to test the hypothesis. Finally, we offer an example from the literature of an experiment grounded in theory, the hypothesis that was tested, and the conclusions the authors were able to draw based on the hypothesis. We conclude by emphasizing that theory development and refinement does not result from a single experiment, but instead requires a process of research that takes time and commitment.

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Scerbo, M.W., Calhoun, A.W., Hui, J. (2019). Research and Hypothesis Testing: Moving from Theory to Experiment. In: Nestel, D., Hui, J., Kunkler, K., Scerbo, M., Calhoun, A. (eds) Healthcare Simulation Research. Springer, Cham. https://doi.org/10.1007/978-3-030-26837-4_22

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7.3: The Research Hypothesis and the Null Hypothesis

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• Michelle Oja
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Hypotheses are predictions of expected findings.

The Research Hypothesis

A research hypothesis is a mathematical way of stating a research question.  A research hypothesis names the groups (we'll start with a sample and a population), what was measured, and which we think will have a higher mean.  The last one gives the research hypothesis a direction.  In other words, a research hypothesis should include:

• The name of the groups being compared.  This is sometimes considered the IV.
• What was measured.  This is the DV.
• Which group are we predicting will have the higher mean.  

There are two types of research hypotheses related to sample means and population means:  Directional Research Hypotheses and Non-Directional Research Hypotheses

Directional Research Hypothesis

If we expect our obtained sample mean to be above or below the other group's mean (the population mean, for example), we have a directional hypothesis. There are two options:

• Symbol:       \( \displaystyle \bar{X} > \mu \)
• (The mean of the sample is greater than than the mean of the population.)
• Symbol:     \( \displaystyle \bar{X} < \mu \)
• (The mean of the sample is less than than mean of the population.)

Example \(\PageIndex{1}\)

A study by Blackwell, Trzesniewski, and Dweck (2007) measured growth mindset and how long the junior high student participants spent on their math homework.  What’s a directional hypothesis for how scoring higher on growth mindset (compared to the population of junior high students) would be related to how long students spent on their homework?  Write this out in words and symbols.

Answer in Words:            Students who scored high on growth mindset would spend more time on their homework than the population of junior high students.

Answer in Symbols:         \( \displaystyle \bar{X} > \mu \) 

Non-Directional Research Hypothesis

A non-directional hypothesis states that the means will be different, but does not specify which will be higher.  In reality, there is rarely a situation in which we actually don't want one group to be higher than the other, so we will focus on directional research hypotheses.  There is only one option for a non-directional research hypothesis: "The sample mean differs from the population mean."  These types of research hypotheses don’t give a direction, the hypothesis doesn’t say which will be higher or lower.

A non-directional research hypothesis in symbols should look like this:    \( \displaystyle \bar{X} \neq \mu \) (The mean of the sample is not equal to the mean of the population).

Exercise \(\PageIndex{1}\)

What’s a non-directional hypothesis for how scoring higher on growth mindset higher on growth mindset (compared to the population of junior high students) would be related to how long students spent on their homework (Blackwell, Trzesniewski, & Dweck, 2007)?  Write this out in words and symbols.

Answer in Words:            Students who scored high on growth mindset would spend a different amount of time on their homework than the population of junior high students.

Answer in Symbols:        \( \displaystyle \bar{X} \neq \mu \) 

See how a non-directional research hypothesis doesn't really make sense?  The big issue is not if the two groups differ, but if one group seems to improve what was measured (if having a growth mindset leads to more time spent on math homework).  This textbook will only use directional research hypotheses because researchers almost always have a predicted direction (meaning that we almost always know which group we think will score higher).

The Null Hypothesis

The hypothesis that an apparent effect is due to chance is called the null hypothesis, written \(H_0\) (“H-naught”). We usually test this through comparing an experimental group to a comparison (control) group.  This null hypothesis can be written as:

\[\mathrm{H}_{0}: \bar{X} = \mu \nonumber \]

For most of this textbook, the null hypothesis is that the means of the two groups are similar.  Much later, the null hypothesis will be that there is no relationship between the two groups.  Either way, remember that a null hypothesis is always saying that nothing is different.  

This is where descriptive statistics diverge from inferential statistics.  We know what the value of \(\overline{\mathrm{X}}\) is – it’s not a mystery or a question, it is what we observed from the sample.  What we are using inferential statistics to do is infer whether this sample's descriptive statistics probably represents the population's descriptive statistics.  This is the null hypothesis, that the two groups are similar.  

Keep in mind that the null hypothesis is typically the opposite of the research hypothesis. A research hypothesis for the ESP example is that those in my sample who say that they have ESP would get more correct answers than the population would get correct, while the null hypothesis is that the average number correct for the two groups will be similar. 

In general, the null hypothesis is the idea that nothing is going on: there is no effect of our treatment, no relation between our variables, and no difference in our sample mean from what we expected about the population mean. This is always our baseline starting assumption, and it is what we seek to reject. If we are trying to treat depression, we want to find a difference in average symptoms between our treatment and control groups. If we are trying to predict job performance, we want to find a relation between conscientiousness and evaluation scores. However, until we have evidence against it, we must use the null hypothesis as our starting point.

In sum, the null hypothesis is always : There is no difference between the groups’ means OR There is no relationship between the variables .

In the next chapter, the null hypothesis is that there’s no difference between the sample mean   and population mean.  In other words:

• There is no mean difference between the sample and population.
• The mean of the sample is the same as the mean of a specific population.
• \(\mathrm{H}_{0}: \bar{X} = \mu \nonumber \)
• We expect our sample’s mean to be same as the population mean.

Exercise \(\PageIndex{2}\)

A study by Blackwell, Trzesniewski, and Dweck (2007) measured growth mindset and how long the junior high student participants spent on their math homework.  What’s the null hypothesis for scoring higher on growth mindset (compared to the population of junior high students) and how long students spent on their homework?  Write this out in words and symbols.

Answer in Words:            Students who scored high on growth mindset would spend a similar amount of time on their homework as the population of junior high students.

Answer in Symbols:    \( \bar{X} = \mu \)

Contributors and Attributions

Foster et al.  (University of Missouri-St. Louis, Rice University, & University of Houston, Downtown Campus)

Dr. MO ( Taft College )

Institutional Architectures to Improve Systems Operations and Management (2012)

Chapter: chapter 2 - background, hypothesis, and methodology.

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18 This chapter highlights the significance of NRC and the level of conventional strategy applications deployed to date. It identifies the business process needs of effective SO&M and develops a hypothesis about the relationship with institutional architecture. Focus on NRC Over the last decade, new metropolitan highway capacity increases have averaged less than 2% per year, outpaced by growth in vehicle miles traveled (VMT). Highway level-of- service (LOS) continues to deteriorate in major metropolitan areas in most states, as growing demand exceeds available capacity. These capacity shortfalls result in increasing, recur- ring (peak) congestion. Given current budgets, as well as environmental and energy constraints, there is little likelihood that new capacity will be made available at the network level to substantially relieve this type of congestion. At the same time, NRC related to crashes, bad weather, high- way construction/maintenance, and special events produce additional delays and disruptions that are largely independent of the capacity situation. Table 2.1 presents the causes of conges- tion by level of urbanization. NRC is responsible for more than half of the total delay and most of the lack of reliability experi- enced on the U.S. highway system. The negative impact of NRC on highway operations is even more pronounced in smaller urban and rural areas. This unpredictability is of special concern in a society that values reliability and just-in-time service. NRC also heightens crash potential. Every minute of lane blockage from crashes, breakdowns, or weather can translate into 3 to 7 min of flow recovery after the lanes are cleared. Secondary crash likelihood increases by 2 to 3% for each minute of queue continuation. Effective Strategy Applications to Reduce NRC Specific effective strategy applications to reduce the impacts of NRC are known but are nowhere near being used to their potential. Minimizing the causes of NRC involves reducing the incidence or the causes of unreliability through either pre-event actions (e.g., speed control, advisories, deicer application) or postevent minimization of the impact of the incidence (e.g., rapid crash clearance, rapid snow removal). The strategy applications themselves combine the following: • ITS applications—typically a control device and communi- cations infrastructure or software-based platforms (capital projects that need to be engineered); • Related procedures and protocols (that need to be devel- oped and documented)—actions are taken in real time by participants in conjunction with the ITS applications; and • Development of concepts of operations as a tool to identify roles, infrastructure, and information transfer (requiring agreement among participants), upon which the procedures and protocols are based. The strategy applications that have been developed for NRC are typically centered within the larger highway jurisdictions— state DOTs, toll entities, and the large local government transportation agencies—together with their public safety partners. Although the focus is often on expressways, the applications are also used for major arterials and rural routes. These conventional strategy applications include the following: • Incident management, including multijurisdictional inte- grated corridor management in response to crashes, break- downs, hazardous materials spills, and other emergencies; • Road weather management in response to heavy rain and wind, snow, and ice; • Work zone traffic management focused on traffic control plans to minimize the impacts of reduced capacity; • Special events planning and management to accommodate event patrons with minimum traffic disruption; and • Active traffic management using lane use and speed con- trol to minimize flow disruption and incidents, as well as managing diversions and the operation of diversion routes. C H A P T E R 2 Background, Hypothesis, and Methodology

19 Table 2.1. Percentage of Contribution to Total Delay in Urban and Nonurban Areas Large Urban Areas Small Urban Areas >1 Million Population 0.1–1.0 Million Population Rural Recurring Causes Network Demand > Capacity 29–37 20–26 0 Poor Signal Timing 4–5 6–10 2 Total Recurring 33–42 26–36 2 Nonrecurring Causes Crashes 35–36 19–26 26 Breakdowns 6–7 6–10 25 Work Zones 8–19 26–27 39 Weather 5–6 7–10 7 Special Events/Poor Information 1 <1 0 Total Nonrecurring 55–69 58–73 97 Source: Summarized in Lockwood, 2006. From FHWA table combining recurring congestion data (TTI) and nonrecurring congestion data (ORNL). Cause of Delay 80 60 40 20 0 A ve ra ge R ed uc tio n in In ci de nt D ur at io n (% ) Fai rfax , V A Ma ryla nd Atla nta , GA Alb uqu erq ue, NM San An ton io, TX Source: FHWA, ITS Benefits and Costs Database. Figure 2.1. Best practice incident management reduction. The first four strategy applications to improve reliability are well understood, and best practices are visible in a several locations. However, active traffic management—the most aggressive approach to avoiding disruption and managing when it happens—is in the early stages of development in both the United States and Western Europe. The Potential of SO&M Regarding NRC As suggested in Figure 2.1 and Table 2.2, the best practice examples provide convincing evidence that these strategy applications can have significant impacts on otherwise deteri- orating service, while providing visible evidence of the agency’s commitment to addressing the mobility challenges facing its customers. Figure 2.1 illustrates a range of impacts on the duration of delays—over 50% reduction in one case—as a result of incident management. Table 2.2 illustrates the broad range of other strategy applications and their impacts. Safety service patrols reduce incident clearance times and related accidents; up-to-date traveler information systems provide improvements in trip reliability; ramp and lane operations management increases throughput; and work zone management minimizes dis- ruption. Of special importance are the high benefit–cost of these strategy applications and the potential for networkwide improvement, compared with the more focused and expen- sive investments in capacity. Systems Operations and Management The concept of SO&M has evolved since the 1991 Intermodal Surface Transportation Efficiency Act (ISTEA). SO&M refers to the broad notion that transportation agencies can apply a set of known strategy applications to maintain and improve highway service in the face of recurring peak-period conges- tion and nonrecurring events such as major crashes, weather, and special event disruptions. There are several excellent best practice examples of SO&M applications on the part of state DOTs in a few major metropolitan areas in the United States. They include highly integrated incident management, well- managed work zone control, and innovative traveler infor- mation programs. However, these examples obscure a more general reality: at the statewide level (even in states with the well-known examples), best practice is confined to one or two congested metropolitan areas, and even in those areas, only a narrow range of strategy applications is applied. Therefore, there is significant opportunity for improving this generally low level of implementation.

20 Table 2.2. Systems Operations Benefits Energy/Environmental Benefits and Benefit–Cost Ratios Safety Impact Mobility Impact Impact Traffic incident management Incident duration reduced 30–50% High High High • Safety service patrols 2:1 to 42:1 High High High • Surveillance and detection 8:1 High High High Road weather information systems 2:1 to 10:1; crash rates reduced High High High from 7–80% Traveler information dynamic 3% decrease in crashes; Low High Low message signs 5–15% improvement in on-time performance Work zone management 2:1 to 40:1; system delays reduced High Medium Medium up to 50% Active Traffic Management Throughput increased by 3–7%; High High Medium decrease in incidents of 3–30% Source: U.S. Department of Transportation, Intelligent Transportation Systems Joint Program Office, 2009. Best practice indicates that important improvements in sys- tem reliability depend largely on the noncapital, noncapacity measures that are at the core of SO&M, and that this is an arena in which transportation agencies can make significant gains even as travel demand grows—despite current financial and construction constraints. Furthermore, the barriers are no longer technical, since most SO&M strategies, systems, and technologies are well understood, even commoditized. What appears to be lacking are features that are normal for other transportation agency (state or local) core programs, such as construction and maintenance (e.g., comprehensive plans and programs, effective technical processes, consistent technology, and robust performance orientation). As can be seen in construction and maintenance, these basic business processes must be supported by a clear mission commitment, visible leadership, organizational alignment, technical capaci- ties, aligned partnerships, and a supportive professional culture. In this project, these nontechnical considerations are defined as the institutional architecture. The Level of SO&M Deployment Related to NRC Over the last 15 years, many states have built transportation management centers (TMCs), installed ITS technologies over increasing segments of their major networks, deployed safety service patrols, and developed interagency approaches to incident management and traveler information. Several states have established benchmarks for the state of the prac- tice in certain of the basic NRC-oriented strategy applications (see Table 2.3). Nevertheless, the state of the practice is uneven. Several states with major metropolitan congestion have made modest progress with only nominal SO&M applications. In many states, some ITS technology has been deployed, but there is a limited commitment to the improvement and implementa- tion of the procedures and development of the partnerships required to capitalize on the technology. Even within individual states, the levels of application are uneven across metropolitan areas, reflecting the limited commitment at the statewide policy level. Furthermore, the level of investment seems to be plateauing. For example, according to the Texas Transportation Institute 2009 Urban Mobility Report, only 74 of 90 cities surveyed have an incident management activity—covering on aver- age less than two-thirds of the highway system. Figure 2.2 indicates the deployment level of basic ITS systems in the top 70 metropolitan areas as determined by a 2008 Bureau of Transportation Statistics survey (U.S. Department of Transportation, Research and Innovative Technology Admin- istration, 2009b). From an investment point of view, few states spend as much as 2% of their total DOT budgets on SO&M and, even in those states, recent financial shortfalls have led to program cuts in some of the most cost-effective activities such as safety service patrols. Meanwhile, the gap between both RC and NRC, and trans- portation agency efforts to manage that congestion and asso- ciated disruptions, is growing. Commitment to Improving SO&M It is apparent that the larger transportation agencies—especially state DOTs—exhibit a strong capital program orientation with a civil engineering culture, organizational structure, internal business processes, and resources that have evolved to support

Table 2.3. Examples of Institutional Best Practice • An increasing number of states have quick clearance laws to support the removal of stopped vehicles from obstructing the road. Florida DOT (FDOT), for example, carried out an aggressive statewide campaign of signage, radio spots, billboards, and brochures to inform the public about the law and its benefits. • Both the FDOT Rapid Incident Scene Clearance (RISC) program and Georgia DOT Towing and Recovery Incentive Program (TRIP) are public– private partnerships that use both incentive payments and disincentive liquidated damages to ensure shortened clearance times for heavy vehicle wrecks; these programs have reduced the average clearance times by 100%. • Oregon DOT has used a set of unique contractor requirements (staged tow trucks, traffic supervision, and public advisories) as part of effective work zone traffic control. • Detroit metropolitan area transportation agencies are part of a regional multiagency coalition that tracks and manages weather problems and treatment strategies, including flexible inter-jurisdictional boundaries for efficient operations. • The 16-state I-95 Corridor Coalition has supported an operations academy, which is a 2-week residential program designed to provide middle and upper managers in state DOTs with a thorough grounding in various aspects of SO&M state of the practice. • The Maryland DOT Coordinated Highways Action Response Team (CHART) program is a formal, multiyear budgeted ITS and operations program with an advisory board that provides oversight and strategic direction. It is chaired by the deputy administrator/chief engineer for operations and including district engineers, the director of the Office of Traffic and Safety, the director of the Office of Maintenance, the Maryland State Police, the Maryland Transportation Authority, the Federal Highway Administration, the University of Maryland Center for Advanced Transportation Technology, and various local governments. • Washington State DOT (WSDOT) has formalized interactions among units and managers involved in its SO&M program. TMC managers from around the state meet every 6 weeks to coordinate with regional Incident Response Program managers, who in turn meet quarterly for operations coordination with the state patrol. TMC managers and incident response managers coordinate activities and issues by meeting with the statewide traffic engineers group and the maintenance engineers group. • The Oregon Transportation Commission moved some capacity funding to the operations program to create an Operations Innovation Program that awards funding to projects selected on a competitive basis for their potential to demonstrate innovative operations concepts related to congestion mitigation and freight mobility. • Virginia DOT has reorganized its senior management to include a deputy director for operations and maintenance responsible for all SO&M activities, as well as maintenance resources. • WSDOT has made a strong and transparent commitment to performance measurement as evidenced by the quarterly Gray Notebook, which tracks performance based on five WSDOT legislative goals, including mobility/congestion, and includes regular updates on progress in the application of operations strategies such as incident management and HOT lanes. 0 10 20 30 40 50 60 1997 2002 2007 Fwy Service Patrol VMS Safety Service Patrol Arterial Surveillance/Detctn Traffic Info Dissemination Closed Loop Signalization Color version of this figure: www.trb.org/Main/Blurbs/165285.aspx. Figure 2.2. Percentage of relevant deployment in urban areas. capacity development and maintenance. This orientation is strongly supported by external constituencies and by a near-complete span of control over the resources necessary to deliver on-time and on-budget capital and maintenance programs. This is not a reflection of transportation agency competence. For example, on average, state DOTs manage large programs with complex processes and make continuous improvements in technology, process, and outcomes. Over the past several decades, transportation agency management has subjected both the project development process and asset management to self-conscious and deliberate reengineering that has supported continuous improvement in competencies, efficiency, and effectiveness. By contrast, SO&M has not yet evolved the same kind of tailored program, business processes, 21

22 relationships, and measures that are required for improved efficiency and effectiveness. Unique Process and Institutional Demands of SO&M Implementing effective congestion management applications makes demands on a transportation agency’s institutional envi- ronment that are at odds with those of capacity development, safety, and maintenance that constitute the legacy context. These demands reflect common and characteristic features of SO&M applications that determine their effectiveness. SO&M applications are typically • Reactive and responsive to unpredictable events on an around-the-clock basis; • Dependent on situational awareness and communications technology; • Applied at the corridor scale or network level; • Based on teamwork and communications intensive; • Dependent on performance monitoring and evaluated through the impact on system performance measured in real time; • Based on the use of dynamic high technology and systems engineering; and • Dependent on outsiders—partners who are not under the control of a transportation agency, including PSAs and local government. Figure 2.3 illustrates these features and the requirements they place on specialized infrastructure, custom-tailored busi- ness processes, and various institutional arrangements. Institutional Reality The failure to capitalize on the potential of SO&M is not for lack of concepts, technology, or even money. Institutional issues are a significant part of this phenomenon. With only some notable exceptions, few transportation agencies have business models committed to making the most effective use of existing capacity. FHWA administers an annual traffic incident manage- ment self-assessment of (TIM SA) for 86 urban jurisdictions (states, regions). This one of the few sources that rate trans- portation agency practices and progress in the program and institutional areas (called “strategic” in the FHWA survey), as well as the more tactical and support-oriented areas related to incident management-specific procedures and protocols. In the strategic area, respondents rate progress in how inci- dent management programs are organized, resourced, tracked supported and sustained. Since incident management is a core strategic for SO&M in general, the assessment provides a useful reflection of current state progress. The self assess- ments indicated that, at the program level, SO&M remains substantially informal regarding program status, formal inter- agency relationships and performance tracking. As reported in the 2009 assessment, “Despite progress in the Strategic Scoping & Performance Monitoring Situation Status Communications and Reporting (Internal and External) Interagency Coordinated Execution of Event Response Activities Real-Time Mobilization of Equipment/ PersonnelSystems Operations & Performance Monitoring Operations Actions Taking Place in Real Time Asset ManagementMaintenance Infrastructure for Control Infrastructure for Situational Awareness Technology and Systems Deployment Systems EngineeringPlan and Program Interagency Coordination Accommodate Program in Portfolio Business Processes Conventional Agency Processes Taking Place in Administrative Time Figure 2.3. Essential process and capabilities to realize SO&M strategy application effectiveness.

area, the five questions receiving the lowest mean score in the TIM SA are in this section” (U.S. Department of Transporta- tion, FHWA, 2009a). The interviews with state DOTs and other transportation managers conducted as part of this project and anecdotal sources indicated that the barriers are substantially institu- tional and are related to the embedded civil engineering culture in transportation agencies; limited understanding of outside stakeholders and decision-makers; state DOT leader- ship with other priorities; organization and staffing oriented to project development and maintenance; funding commit- ments; and unaligned partners. The Importance of Institutional Architecture There has been considerable speculation about the slow pace of mainstreaming SO&M as a formal, state transportation agency core program, especially given its low cost and effectiveness. Even though the concepts and technologies are increasingly well understood, there remains a substantial gap between best practice and average practice within and among states. The slow uptake on this potential by transportation agencies is, therefore, not a result of lack of technical understanding— or from an absence of available best practice models. It is increasingly clear that the current modest focus on SO&M is substantially a product of the conventional legacy context of many transportation agencies today—a civil engineering culture and an inherited organization structured for con- struction and maintenance—the existing capital programs’ claims on scarce resources and difficulties in forging the necessary partnerships with outside entities. These factors of culture, leadership, priorities, organization and staffing, resources, and relationships constitute the institutional setting for change in the existing transportation agencies—both state DOTs and other major highway entities. The term “institutional architecture” has been applied to the overall configuration of these elements in a transportation entity context. However, until institutional architecture is defined and analyzed by its components and until the dynamics of, and relationships among, those components are clarified, it cannot become the subject for useful discussion or manage- ment. A major focus of this project, therefore, is to define and describe institutional architecture, so that it can be subject to change management. In this project, institutional architecture focuses on the substantial nontechnical features that describe whether, how, and with whom an agency pursues SO&M. It is there- fore important to distinguish institutional architecture from technical and business processes (such as planning/program- ming, systems development, and performance measurement) and from the program of SO&M applications—such as incident management or road weather information. The research in this report does include determination of the common aspects of the programs and technical and business processes of the states that have more effective oper- ations but only to the extent that those processes identify the needed institutional architecture. For example, an effec- tive incident management program requires an interrelated sequence of planning, systems engineering, resource allocation, procurement, project development and implementation, procedural coordination, and so forth. All these processes, in turn, depend on key elements of a supportive institutional setting—leadership, legal authorization, organized responsi- bilities, staff capabilities, available resources, and working partnerships. This report focuses on the institutional impli- cations; it does not provide program or process guidance. Institutional architecture encompasses more than just agency organization. It includes leadership, staffing, resources, partnerships, and the prevailing culture. Culture, in partic- ular, is a key element of institutional architecture as it refers to the values, assumptions, and priorities of the agency, agency staff and leadership, the expectations of users, and the policy environment. It is the pervasive legacy culture of transportation agencies that is least susceptible to management and it is the slowest component of institutional architecture to change. It is the premise of this project to capitalize on the full potential of SO&M, which is substantially dependent on the level of support provided by theses institutional features. Basic Hypothesis of the Report As indicated, the business process characteristics needed for an effective SO&M program are substantially different from those associated with the traditional transportation agency capital programs. It is a reasonable assumption that these characteristic processes make special institutional demands on leadership, organization, staffing, resources, and relation- ships. These demands might be different from those around which transportation agency conventions and configurations have formed, especially if the needed business processes are to be mainstreamed within the agency’s normal activities on a continuing basis. To develop a more structured understanding of these relationships, this research was conducted in three parts: • Identification of the apparently more effective transportation agency programs via known program characteristics; • Determination of the technical and business process fea- tures that are needed to support program effectiveness (through interview and secondary materials); and 23

24 • Identification of the institutional characteristics that appear to be essential in the development, support, and sustainment of the key process features. The primary focus of this report is the institutional frame- work. Processes (business and technical) are identified as part of the research, but only to clarify the needed institutional architecture. Neither program nor process-specific guidance is presented. Study Methodology The overall methodology used to identify the chain of influence between institutional architecture and program effectiveness follows and is described in Chapters 3 through 7. The core of this analysis was the identification of traceable relationships from the more effective transportation agency SO&M pro- grams to institutional architecture through the medium of business processes. A general hypothesis was developed that institutional architecture is related to the level of consistency by which agency business processes support effective programs— and specifically the business processes that support SO&M programs (see Figure 2.4). A review of organizational development theory was used to assist in pinpointing key process and institutional features that differentiate service and operations-oriented organizations from those with a project or product focus (in addition, key concepts from the process improvement literature suggested a framework for change management). A general range in transportation agency SO&M program effectiveness was derived from information available about various transportation agency program activities, processes, and effectiveness. Certain states were clustered into a group appearing to have more fully developed programs—which are called mature states—and a group with SO&M activities transitioning toward fuller programs, called transitioning states. A survey of selected state DOTs (from both groups) was conducted to determine relationships between program effectiveness and key business process and institutional factors. The survey questionnaire was structured around the basic hypothesis and the indications from the organizational devel- opment literature. The conclusions from the survey and research identify- ing the key variables of SO&M-related business processes essential to effective programs and that related most closely to more effective processes were documented as the basis for determining the features of institutional architecture needed to support these processes (which, in turn, enable effective programs and structured into a capability maturity model form). FRAMEWORK PROCESSES PROGRAM INSTITUTIONAL The values, capabilities, arrangements, and resources to support and sustain the program qualities below. The business processes and systems required to facilitate required business processes. A needs-responsive, performance- driven, comprehensive, cost-effective, statewide SO&M program. Figure 2.4. Basic hypothesis.

Self- Assessments and Interviews A Institutional rchitecture Findings Managing Institutional Change Guidance The Capability Maturity Model Process Findings Organizational and Change Management Theory Alternative Institutional Models Background Hypothesis & Methodology Report Figure 2.5. Study methodology. The process levels indicated were then used in combination with interview indications and the insights of organization development theory and relevant international practice to identify and structure institutional elements into incremental capability maturity model processes. A guidance framework for institutional change, based on self-evaluation and change management opportunities, was developed. Overall, change management scenarios were defined and illustrated in which the guidance can be applied ranging from incremental to event driven (Chapter 8). Alternative institutional models to incremental change were reviewed and detailed guidance was prepared that focused on the specific set of strategies needed to transition each of the four key elements to the next level of maturity. These strategies are presented in the accompanying guide. See Figure 2.5 for an illustration of this methodology. 25

TRB’s second Strategic Highway Research Program (SHRP 2) Report S2-L06-RR-1: Institutional Architectures to Improve Systems Operations and Management examines a large number of topics concerning organizational and institutional approaches that might help transportation agencies enhance highway operations and travel time reliability.

The same project that produced SHRP 2 Report S2-L06-RR-1 also produced SHRP 2 Report S2-L06-RR-2 : Guide to Improving Capability for Systems Operations and Management.

An e-book version of this report is available for purchase at Google , iTunes , and Amazon .

An article on SHRP 2 Report S2-L06-RR-1 was published in the January-February 2013 issue of the TR News.

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China’s Approach to Foreign Policy Gets Largely Negative Reviews in 24-Country Survey

Still, views of china – and its soft power – are more positive in middle-income countries, table of contents.

• Road map to the report
• How views of China have changed in recent years
• Views of China by age group
• How views of China’s international behavior have changed over time
• Most say China does not contribute to world peace and stability
• China seen as interventionist
• How opinions about which country is the world’s top economy have changed in recent years
• Many who see China as the world’s leading economic power also see it as a good thing
• Chinese investment seen as an economic benefit
• How views of Chinese soft power vary by age
• Views of Chinese technology
• How confidence in Xi has changed over time
• How confidence in Xi varies by age
• Acknowledgments
• Methodology

This Pew Research Center analysis focuses on public opinion of China and President Xi Jinping in 24 countries in North America, Europe, the Middle East, the Asia-Pacific region, sub-Saharan Africa and Latin America. The report explores views of China’s role in the world, including as an economic power, and perceptions of Chinese soft power. This is the first year since 2019 that the Global Attitudes Survey has included countries from Africa and Latin America, which were not included more recently due to the coronavirus outbreak .

For non-U.S. data, this report draws on nationally representative surveys of 27,285 adults conducted from Feb. 20 to May 22, 2023. All surveys were conducted over the phone with adults in Canada, France, Germany, Greece, Italy, Japan, Netherlands, South Korea, Spain, Sweden and the United Kingdom. Surveys were conducted face to face in Argentina, Brazil, Hungary, India, Indonesia, Israel, Kenya, Mexico, Nigeria, Poland and South Africa. In Australia, we used a mixed-mode probability-based online panel.

In the United States, we surveyed 3,576 U.S. adults from March 20 to 26, 2023. Everyone who took part in this survey is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

Here are the questions used for the report , along with responses, and the survey methodology .

Views of China are broadly negative across 24 countries in a new Pew Research Center survey: A median of 67% of adults express unfavorable views of the country, while 28% have a favorable opinion.

Negative views extend to evaluations of China’s international actions. Despite several high-profile diplomatic initiatives by Beijing over the past year – such as brokering a peace deal between Saudi Arabia and Iran and issuing a 12-point proposal for the end of violence in Ukraine – a median of 71% think China does not contribute to global peace and stability.

Most people also think China does not take into account the interests of other countries in its foreign policy (76%) and a median of 57% say China interferes in the affairs of other nations a great deal or fair amount.

Still, attitudes toward China are somewhat rosier in middle-income than high-income countries. Across eight middle-income countries – places Pew Research Center has not surveyed since 2019 due to the challenges of conducting face-to-face interviews during the pandemic – India stands out as the only middle-income country in which a majority has unfavorable views of China. And in three middle-income countries – Kenya, Mexico and Nigeria – a majority even gives China a positive rating.

Fewer in these middle-income countries also criticize China’s global behavior, and many more see China’s “soft power” appeal. Indeed, publics in these middle-income countries offer relatively favorable ratings for China’s entertainment products, its universities and its standard of living – while few in most high-income countries agree.

Across all 24 countries surveyed, however, there is more agreement about China’s technology. A median of 69% describe China’s technological achievements as the best or above average relative to other wealthy nations, with similar shares in high- and middle-income countries. A median of 54% also see China’s military as among the best in the world.

But views of the country as the world’s foremost economic power have faltered somewhat in recent years. More people now name the United States as the top economic power than China (a median of 42% vs. 33%, respectively). Much of this shift has come in high-income countries, where the share naming China has fallen in nearly every surveyed country – including by double digits in Germany, the Netherlands, Poland and Sweden.

In the U.S., where equal shares (43%) called China and the U.S. the world’s leading economic power in 2022, views have shifted significantly over the past year ; now, Americans are 10 percentage points more likely to name the U.S. than China (48% vs. 38%). (For more on American views of China, read “ Americans are Critical of China’s Global Role – as Well as Its Relationship With Russia ”.)

These findings come from a new Pew Research Center survey conducted from Feb. 20 to May 22, 2023, among more than 30,000 people in 24 countries. Below are some of the other findings regarding China’s overall image, views of Chinese foreign policy, ratings of President Xi Jinping, opinions about Chinese soft power and its economic power.

Overall ratings for China

Across many high-income countries surveyed, which are in North America, Western Europe and parts of the Asia-Pacific region, a large majority has unfavorable views of China, as has been the case for multiple years . Indeed, in almost every high-income country surveyed, negative views currently stand at or near historic highs. In most countries, this does not reflect a significant increase over last year; rather, negative views have simply remained high in recent years. One notable exception is Poland, where negative views have increased 12 points during a period of strained bilateral relations , perhaps related to China’s handling of the war in Ukraine.

Views of China in middle-income countries are relatively more positive. Still, negative ratings in most of these countries have also grown since the countries were last surveyed, pre-pandemic. In South Africa and Mexico, for example, opinions have turned somewhat more negative since 2019, and in Argentina, Brazil and India, negative views have even reached historic highs. In India, military conflicts along a contested border may have contributed to the 21 percentage point increase in unfavorable opinion.

China’s role on the world stage

Majorities in most countries do not think China takes into account the interests of countries like theirs. In Canada, France, Israel, Spain and Sweden, around half or more say China doesn’t consider them at all . Only in the three sub-Saharan African countries surveyed, as well as in Indonesia, does around half or more of the public feel like China listens to their country.

A median of 71% also think China does little or nothing at all to contribute to global peace and stability, compared with a median of 23% who say it is doing a great deal or a fair amount. Australians, Canadians, Indians, Israelis and South Koreans are particularly likely to say China is doing nothing at all to help with global peace and stability.

Most also see China as an interventionist power. A median of 57% say China does interfere a great deal or a fair amount in the affairs of other countries, while a median of 35% say it does not do so much or at all. Around seven-in-ten or more in Australia, Canada, Japan, South Korea, Spain and the U.S. see China getting involved in the affairs of other countries – and many of these places also stood out for the high share who said China’s involvement in domestic politics in their own country was a very serious problem in a 2022 Pew Research Center survey .

But the country which is most likely to see China interfering in the affairs of other countries in this year’s survey is Italy (82%). Italy, which was the only G7 country to join China’s Belt and Road Initiative (BRI) , was debating leaving the initiative at the time that the survey was conducted, but treading delicately for fear of stoking possible Chinese retribution against Italian businesses.

Attitudes toward Xi

Few in the 24 countries surveyed have confidence in Chinese President Xi Jinping to do the right thing regarding world affairs. Across most of Western Europe, the U.S., Canada and much of the Asia-Pacific region, around half in each country say they have no confidence in him at all . Indonesia, Kenya, Nigeria and South Africa stand out as the only countries where a majority or plurality have confidence in his leadership.

Confidence in Xi is closely related to views of China more broadly. In each country surveyed people with unfavorable views of China are more likely to have little confidence in the Chinese president, and vice versa.

Chinese soft power

When it comes to elements often considered part of a country’s “soft power,” China’s technological achievements receive high marks, though fewer say the same about its universities, entertainment products or standard of living.

In fact, outside of South Korea, nearly half or more in every country say Chinese technological advancements are the best in the world or above average relative to other wealthy nations. And in many of the middle-income countries, around four-in-ten call Chinese technology the best in the world.

Middle-income countries – many of which are increasingly reliant on Chinese companies like Huawei for components of their 4G and 5G systems – were also asked specifically about technology such as phones, tablets or computers made by Chinese companies. Across these eight countries, there is a relatively widespread sense that these products are well-made. Middle-income publics are more divided when it comes to their cost: A median of 50% describe them as inexpensive, while 44% call them costly.

They are also somewhat divided when it comes to whether technological products made by Chinese companies protect people’s personal data (a median of 45%) or make their data unsafe (40%). (Americans were asked a different but related question about Chinese social media companies; large majorities have little confidence that they will use personal information responsibly or follow privacy policies.)

In every country, at least a plurality – and often a majority – also see China’s “hard power,” its military, as one of the best in the world or above average.

Chinese economic power

Fewer name China as the world’s leading economic power than the U.S. (a median of 33% vs. 42%). And, in many countries, the share naming China as the world’s leading economy has gone down in recent years.

Interestingly, China’s image as an economic superpower is stronger in high-income countries than middle-income ones. Italy, for example, is the only country where a majority (55%) calls China the leading economic power.

Still, people in middle-income countries do recognize economic benefits from their relations with China. A different survey question, asked only in these countries, finds that around half or more in six middle-income countries say their nation’s economy has benefited a great deal or a fair amount from Chinese investment. In Nigeria, Kenya and South Africa, around seven-in-ten or more say this.

In the U.S., Americans were also asked to name the country which poses the top threat to the U.S. Not only was China the top answer, by far, but Americans see it as both an economic and a national security threat – in sharp contrast to Russia, which is primarily seen as a security threat. To read more about this related analysis, see “ Americans name China as the top threat facing the U.S. ”

The chapters that follow discuss these findings and others in more detail:

• Chapter 1 looks at overall opinion of China across the countries surveyed, including how perceptions have shifted over the years
• Chapter 2 considers the negative and positive roles China plays in international affairs
• Chapter 3 reviews global public opinion about which country is the world’s leading economic power
• Chapter 4 explores perceptions of Chinese soft power, summarizing how people across 24 countries rate China compared with other wealthy nations
• Chapter 5 examines confidence in Chinese President Xi Jinping to do the right thing in world affairs

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Americans Remain Critical of China

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May 15, 2024

Purdue commercial air service underway following inaugural flight, groundbreaking

The first Southern Airways Express flight departed Wednesday (May 15) from Purdue University Airport, traveling to O’Hare International Airport. This marks the return of commercial air service to the Purdue Airport for the first time since 2004 and followed a ceremonial first flight and groundbreaking event held Tuesday (May 14) for the university’s new Amelia Earhart Terminal. (Purdue University photo/Kelsey Lefever)

WEST LAFAYETTE, Ind. — Commercial air service has returned to the Purdue University Airport, marking the next chapter in the university’s storied tradition of aviation.

At 6:20 a.m. Wednesday (May 15), the first Southern Airways Express flight departed Purdue University Airport (LAF) in West Lafayette, traveling to Chicago’s O’Hare International Airport (ORD). Southern Airways will operate flights between the two airports seven days a week, with 24 weekly round-trip flights currently planned. Purdue and Surf Air Mobility Inc. (NYSE: SRFM) agreed last December to begin scheduled commuter air service between LAF and ORD.

Wednesday’s flights mark the return of commercial air service to the Purdue University Airport for the first time since 2004 and follow a ceremonial first flight and groundbreaking event held Tuesday (May 14) for the university’s new Amelia Earhart Terminal.

ADDITIONAL INFORMATION

• Commercial flights take off from Purdue Airport (Instagram reel)
• Taking flight at Purdue University
• Purdue plans groundbreaking for Earhart Terminal, inaugural flight of commercial air service
• Flight booking information

At Tuesday’s event, Purdue University officials were joined by Southern Airways Express leaders and community and industry representatives to celebrate a ceremonial groundbreaking for the new Amelia Earhart Terminal in Hangar 2 at Purdue University Airport. Rob Wynkoop, vice president of Purdue Administrative Operations, provided opening remarks and was followed by Stan Little, founder and CEO of Southern Airways Express; Myung Kim, acting chief of staff for the Transportation Security Administration; and Purdue President Mung Chiang, who spoke about the partnership and what it means for the future.

After the groundbreaking, Chiang, Little, members of the university’s Board of Trustees and Greater Lafayette community leaders boarded a Purdue-branded Southern Airways plane and made a local, ceremonial flight to mark the occasion. 

“Bringing commercial airline services to Purdue University Airport is a milestone that reflects the continued momentum of economic growth of Greater Lafayette and will further accelerate job creation and enhance quality of life for our neighbors,” Chiang said. “As we break ground also for the new Amelia Earhart Terminal at LAF, we thank all the partners at federal, state and local levels and Southern Airways Express as we embark on the next journey following a long tradition of ‘Purdue flies.’”

The return of commercial flights furthers Chiang’s ABCD strategy , in which the “A” stands for airport and aims to provide the university’s students, faculty, staff and families, as well as residents and businesses in Greater Lafayette, with easier, faster connections.

As the local connection to the world, round-trip flights from West Lafayette to O’Hare can provide access to more than 240 connecting destinations. When flying out of LAF, travelers can enjoy numerous benefits, including:

• Shorter security lines and less time in the airport.
• Flight time under an hour compared to a multi-hour drive to ORD.
• Bus fare or gas savings.
• Cheaper parking rates than other airports in the region.
• Interline partnership and seamless connections via American, United, Alaska and Hawaiian airlines.
• Ability to walk gate-to-gate in ORD without going through security with interline airlines.

“I would like to recognize and thank all of the individuals whose persistence made it possible for us to be here today,” Wynkoop said. “Each of you played a critical role in bringing commercial air service back to our community, and we’ll soon have a new terminal building that will not only support this service but will honor a legend in aviation and someone who embodied the Boilermaker spirit, Amelia Earhart.”

Construction on the new, $11.8 million, approximately 9,400-square-foot terminal building will commence this month with completion expected in August 2025. Planned to be located directly west of the existing airport terminal, the Amelia Earhart Terminal will include ticketing, passenger screening, baggage claim and a waiting area to comply with Transportation Security Administration and Federal Aviation Administration requirements.

“Southern Airways is honored to partner with Purdue, America’s leading aviation university, as we not only bring commercial flights back to West Lafayette for the first time in a generation, but also develop the next generation of airline leaders,” Little said. “The CEOs of tomorrow are the Purdue graduates of today, and I’m excited about that.”

Information about booking flights can be found here: https://www.purdue.edu/airport/flight/ .

Media contact: Tim Doty, [email protected]

Note to journalists: Video b-roll from the commercial flight ceremony is available via Google Drive .

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