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How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

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

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

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

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

Variables in hypotheses

Hypotheses propose a relationship between two or more variables . An independent variable is something the researcher changes or controls. A dependent variable is something the researcher observes and measures.

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

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Step 1: ask a question.

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

Step 2: Do some preliminary research

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

At this stage, you might construct a conceptual framework to identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalise more complex constructs.

Step 3: Formulate your hypothesis

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

Step 4: Refine your hypothesis

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

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

Step 5: Phrase your hypothesis in three ways

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

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

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

Step 6. Write a null hypothesis

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

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

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

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

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Research Questions & Hypotheses

Generally, in quantitative studies, reviewers expect hypotheses rather than research questions. However, both research questions and hypotheses serve different purposes and can be beneficial when used together.

Research Questions

Clarify the research’s aim (farrugia et al., 2010).

• Research often begins with an interest in a topic, but a deep understanding of the subject is crucial to formulate an appropriate research question.
• Descriptive: “What factors most influence the academic achievement of senior high school students?”
• Comparative: “What is the performance difference between teaching methods A and B?”
• Relationship-based: “What is the relationship between self-efficacy and academic achievement?”
• Increasing knowledge about a subject can be achieved through systematic literature reviews, in-depth interviews with patients (and proxies), focus groups, and consultations with field experts.
• Some funding bodies, like the Canadian Institute for Health Research, recommend conducting a systematic review or a pilot study before seeking grants for full trials.
• The presence of multiple research questions in a study can complicate the design, statistical analysis, and feasibility.
• It’s advisable to focus on a single primary research question for the study.
• The primary question, clearly stated at the end of a grant proposal’s introduction, usually specifies the study population, intervention, and other relevant factors.
• The FINER criteria underscore aspects that can enhance the chances of a successful research project, including specifying the population of interest, aligning with scientific and public interest, clinical relevance, and contribution to the field, while complying with ethical and national research standards.
• The P ICOT approach is crucial in developing the study’s framework and protocol, influencing inclusion and exclusion criteria and identifying patient groups for inclusion.
• Defining the specific population, intervention, comparator, and outcome helps in selecting the right outcome measurement tool.
• The more precise the population definition and stricter the inclusion and exclusion criteria, the more significant the impact on the interpretation, applicability, and generalizability of the research findings.
• A restricted study population enhances internal validity but may limit the study’s external validity and generalizability to clinical practice.
• A broadly defined study population may better reflect clinical practice but could increase bias and reduce internal validity.
• An inadequately formulated research question can negatively impact study design, potentially leading to ineffective outcomes and affecting publication prospects.

Checklist: Good research questions for social science projects (Panke, 2018)

Research Hypotheses

Present the researcher’s predictions based on specific statements.

• These statements define the research problem or issue and indicate the direction of the researcher’s predictions.
• Formulating the research question and hypothesis from existing data (e.g., a database) can lead to multiple statistical comparisons and potentially spurious findings due to chance.
• The research or clinical hypothesis, derived from the research question, shapes the study’s key elements: sampling strategy, intervention, comparison, and outcome variables.
• Hypotheses can express a single outcome or multiple outcomes.
• After statistical testing, the null hypothesis is either rejected or not rejected based on whether the study’s findings are statistically significant.
• Hypothesis testing helps determine if observed findings are due to true differences and not chance.
• Hypotheses can be 1-sided (specific direction of difference) or 2-sided (presence of a difference without specifying direction).
• 2-sided hypotheses are generally preferred unless there’s a strong justification for a 1-sided hypothesis.
• A solid research hypothesis, informed by a good research question, influences the research design and paves the way for defining clear research objectives.

Types of Research Hypothesis

• In a Y-centered research design, the focus is on the dependent variable (DV) which is specified in the research question. Theories are then used to identify independent variables (IV) and explain their causal relationship with the DV.
• Example: “An increase in teacher-led instructional time (IV) is likely to improve student reading comprehension scores (DV), because extensive guided practice under expert supervision enhances learning retention and skill mastery.”
• Hypothesis Explanation: The dependent variable (student reading comprehension scores) is the focus, and the hypothesis explores how changes in the independent variable (teacher-led instructional time) affect it.
• In X-centered research designs, the independent variable is specified in the research question. Theories are used to determine potential dependent variables and the causal mechanisms at play.
• Example: “Implementing technology-based learning tools (IV) is likely to enhance student engagement in the classroom (DV), because interactive and multimedia content increases student interest and participation.”
• Hypothesis Explanation: The independent variable (technology-based learning tools) is the focus, with the hypothesis exploring its impact on a potential dependent variable (student engagement).
• Probabilistic hypotheses suggest that changes in the independent variable are likely to lead to changes in the dependent variable in a predictable manner, but not with absolute certainty.
• Example: “The more teachers engage in professional development programs (IV), the more their teaching effectiveness (DV) is likely to improve, because continuous training updates pedagogical skills and knowledge.”
• Hypothesis Explanation: This hypothesis implies a probable relationship between the extent of professional development (IV) and teaching effectiveness (DV).
• Deterministic hypotheses state that a specific change in the independent variable will lead to a specific change in the dependent variable, implying a more direct and certain relationship.
• Example: “If the school curriculum changes from traditional lecture-based methods to project-based learning (IV), then student collaboration skills (DV) are expected to improve because project-based learning inherently requires teamwork and peer interaction.”
• Hypothesis Explanation: This hypothesis presumes a direct and definite outcome (improvement in collaboration skills) resulting from a specific change in the teaching method.
• Example : “Students who identify as visual learners will score higher on tests that are presented in a visually rich format compared to tests presented in a text-only format.”
• Explanation : This hypothesis aims to describe the potential difference in test scores between visual learners taking visually rich tests and text-only tests, without implying a direct cause-and-effect relationship.
• Example : “Teaching method A will improve student performance more than method B.”
• Explanation : This hypothesis compares the effectiveness of two different teaching methods, suggesting that one will lead to better student performance than the other. It implies a direct comparison but does not necessarily establish a causal mechanism.
• Example : “Students with higher self-efficacy will show higher levels of academic achievement.”
• Explanation : This hypothesis predicts a relationship between the variable of self-efficacy and academic achievement. Unlike a causal hypothesis, it does not necessarily suggest that one variable causes changes in the other, but rather that they are related in some way.

Tips for developing research questions and hypotheses 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.
• Ensure that the research question and objectives are answerable, feasible, and clinically relevant.

If your research hypotheses are derived from your research questions, particularly when multiple hypotheses address a single question, it’s recommended to use both research questions and hypotheses. However, if this isn’t the case, using hypotheses over research questions is advised. It’s important to note these are general guidelines, not strict rules. If you opt not to use hypotheses, consult with your supervisor for the best approach.

Farrugia, P., Petrisor, B. A., Farrokhyar, F., & Bhandari, M. (2010). Practical tips for surgical research: Research questions, hypotheses and objectives.  Canadian journal of surgery. Journal canadien de chirurgie ,  53 (4), 278–281.

Hulley, S. B., Cummings, S. R., Browner, W. S., Grady, D., & Newman, T. B. (2007). Designing clinical research. Philadelphia.

Panke, D. (2018). Research design & method selection: Making good choices in the social sciences.  Research Design & Method Selection , 1-368.

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What is the difference between a thesis & a hypothesis?

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B oth the hypothesis statement and the thesis statement answer the research question of the study.  When the statement is one that can be proved or disproved, it is an hypothesis statement.  If, instead, the statement specifically shows the intentions/objectives/position of the researcher, it is a thesis statement.

A hypothesis is a statement that can be proved or disproved.  It is typically used in quantitative research and predicts the relationship between variables.

A thesis statement is a short, direct sentence that summarizes the main point or claim of an essay or research paper. It is seen in quantitative, qualitative, and mixed methods research.  A thesis statement is developed, supported, and explained in the body of the essay or research report by means of examples and evidence.

Every research study should contain a concise and well-written thesis statement. If the intent of the study is to prove/disprove something, that research report will also contain an hypothesis statement.

Jablonski , Judith. What is the difference between a thesis statement and an hypothesis statement? Online Library. American Public University System. Jun 16, 2014. Web.   http://apus.libanswers.com/faq/2374

Let’s say you are interested in the conflict in Darfur, and you conclude that the issues you wish to address include the nature, causes, and effects of the conflict, and the international response. While you could address the issue of international response first, it makes the most sense to start with a description of the conflict, followed by an exploration of the causes, effects, and then to discuss the international response and what more could/should be done.

This hypothetical example may lead to the following title, introduction, and statement of questions:

Conflict in Darfur: Causes, Consequences, and International Response       This paper examines the conflict in Darfur, Sudan. It is organized around the following questions: (1) What is the nature of the conflict in Darfur? (2) What are the causes and effects of the conflict? (3) What has the international community done to address it, and what more could/should it do?

Following the section that presents your questions and background, you will offer a set of responses/answers/(hypo)theses. They should follow the order of the questions. This might look something like this, “The paper argues/contends/ maintains/seeks to develop the position that...etc.” The most important thing you can do in this section is to present as clearly as possible your best thinking on the subject matter guided by course material and research. As you proceed through the research process, your thinking about the issues/questions will become more nuanced, complex, and refined. The statement of your theses will reflect this as you move forward in the research process.

So, looking to our hypothetical example on Darfur:

The current conflict in Darfur goes back more than a decade and consists of fighting between government-supported troops and residents of Darfur. The causes of the conflict include x, y, and z. The effects of the conflict have been a, b, and c. The international community has done 0, and it should do 1, 2, and 3.

Once you have setup your thesis you will be ready to begin amassing supporting evidence for you claims. This is a very important part of the research paper, as you will provide the substance to defend your thesis.

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Comparative Studies

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• Mario Coccia 2 , 3 &
• Igor Benati 3  

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Comparative analysis ; Comparative approach

Comparative is a concept that derives from the verb “to compare” (the etymology is Latin comparare , derivation of par = equal, with prefix com- , it is a systematic comparison). Comparative studies are investigations to analyze and evaluate, with quantitative and qualitative methods, a phenomenon and/or facts among different areas, subjects, and/or objects to detect similarities and/or differences.

Introduction: Why Comparative Studies Are Important in Scientific Research

Natural sciences apply the method of controlled experimentation to test theories, whereas social and human sciences apply, in general, different approaches to support hypotheses. Comparative method is a process of analysing differences and/or similarities betwee two or more objects and/or subjects. Comparative studies are based on research techniques and strategies for drawing inferences about causation and/or association of factors that are similar or...

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Benati I, Coccia M (2017) General trends and causes of high compensation of government managers in the OECD countries. Int J Public Adm. doi: https://doi.org/10.1080/01900692.2017.1318399

Benati I, Coccia M (2018) Rewards in Bureaucracy and Politics. In Global Encyclopedia of Public Administration, Public Policy, and Governance –section Bureaucracy (edited by Ali Farazmand) Chapter No: 3417-1, https://doi.org/10.1007/978-3-319-31816-5_3417-1 , Springer International Publishing AG

Coccia M, Rolfo S (2007) How research policy changes can affect the organization and productivity of public research institutes. Journal of Comparative Policy Analysis, Research and Practice, 9(3): 215–233. https://doi.org/10.1080/13876980701494624

Coccia M, Rolfo S (2013) Human Resource Management and Organizational Behavior of Public Research Institutions. International Journal of Public Administration, 36(4): 256–268, https://doi.org/10.1080/01900692.2012.756889

Cooksey RW (2007) Illustrating statistical procedures. Tilde University Press, Prahran

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Gomm R, Hammersley M, Foster P (eds) (2000) Case study method. SAGE Publications, London

Hague R, Harrop M (2004) Comparative government and politics. Palgrave Macmillan, New York

Levine CH, Peters BG, Thompson FJ (1990) Public administration: challenges, choices, consequences. Scott Foresman and Company, Glenview

Peters BG (1998) Comparative politics-theory and method. Macmillan Press, London

Peters BG, Pierre J (2016) Comparative governance: rediscovering the functional dimension of governing. Cambridge University Press, Cambridge

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Coccia, M., Benati, I. (2018). Comparative Studies. In: Farazmand, A. (eds) Global Encyclopedia of Public Administration, Public Policy, and Governance. Springer, Cham. https://doi.org/10.1007/978-3-319-31816-5_1197-1

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What is comparative analysis? A complete guide

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18 April 2023

Reviewed by

Jean Kaluza

Comparative analysis is a valuable tool for acquiring deep insights into your organization’s processes, products, and services so you can continuously improve them. 

Similarly, if you want to streamline, price appropriately, and ultimately be a market leader, you’ll likely need to draw on comparative analyses quite often.

When faced with multiple options or solutions to a given problem, a thorough comparative analysis can help you compare and contrast your options and make a clear, informed decision.

If you want to get up to speed on conducting a comparative analysis or need a refresher, here’s your guide.

Make comparative analysis less tedious

Dovetail streamlines comparative analysis to help you uncover and share actionable insights

• What exactly is comparative analysis?

A comparative analysis is a side-by-side comparison that systematically compares two or more things to pinpoint their similarities and differences. The focus of the investigation might be conceptual—a particular problem, idea, or theory—or perhaps something more tangible, like two different data sets.

For instance, you could use comparative analysis to investigate how your product features measure up to the competition.

After a successful comparative analysis, you should be able to identify strengths and weaknesses and clearly understand which product is more effective.

You could also use comparative analysis to examine different methods of producing that product and determine which way is most efficient and profitable.

The potential applications for using comparative analysis in everyday business are almost unlimited. That said, a comparative analysis is most commonly used to examine

Emerging trends and opportunities (new technologies, marketing)

Competitor strategies

Financial health

Effects of trends on a target audience

Free AI content analysis generator

Make sense of your research by automatically summarizing key takeaways through our free content analysis tool.

• Why is comparative analysis so important? 

Comparative analysis can help narrow your focus so your business pursues the most meaningful opportunities rather than attempting dozens of improvements simultaneously.

A comparative approach also helps frame up data to illuminate interrelationships. For example, comparative research might reveal nuanced relationships or critical contexts behind specific processes or dependencies that wouldn’t be well-understood without the research.

For instance, if your business compares the cost of producing several existing products relative to which ones have historically sold well, that should provide helpful information once you’re ready to look at developing new products or features.

• Comparative vs. competitive analysis—what’s the difference?

Comparative analysis is generally divided into three subtypes, using quantitative or qualitative data and then extending the findings to a larger group. These include

Pattern analysis —identifying patterns or recurrences of trends and behavior across large data sets.

Data filtering —analyzing large data sets to extract an underlying subset of information. It may involve rearranging, excluding, and apportioning comparative data to fit different criteria. 

Decision tree —flowcharting to visually map and assess potential outcomes, costs, and consequences.

In contrast, competitive analysis is a type of comparative analysis in which you deeply research one or more of your industry competitors. In this case, you’re using qualitative research to explore what the competition is up to across one or more dimensions.

For example

Service delivery —metrics like the Net Promoter Scores indicate customer satisfaction levels.

Market position — the share of the market that the competition has captured.

Brand reputation —how well-known or recognized your competitors are within their target market.

• Tips for optimizing your comparative analysis

Conduct original research

Thorough, independent research is a significant asset when doing comparative analysis. It provides evidence to support your findings and may present a perspective or angle not considered previously. 

Make analysis routine

To get the maximum benefit from comparative research, make it a regular practice, and establish a cadence you can realistically stick to. Some business areas you could plan to analyze regularly include:

Profitability

Competition

Experiment with controlled and uncontrolled variables

In addition to simply comparing and contrasting, explore how different variables might affect your outcomes.

For example, a controllable variable would be offering a seasonal feature like a shopping bot to assist in holiday shopping or raising or lowering the selling price of a product.

Uncontrollable variables include weather, changing regulations, the current political climate, or global pandemics.

Put equal effort into each point of comparison

Most people enter into comparative research with a particular idea or hypothesis already in mind to validate. For instance, you might try to prove the worthwhileness of launching a new service. So, you may be disappointed if your analysis results don’t support your plan.

However, in any comparative analysis, try to maintain an unbiased approach by spending equal time debating the merits and drawbacks of any decision. Ultimately, this will be a practical, more long-term sustainable approach for your business than focusing only on the evidence that favors pursuing your argument or strategy.

Writing a comparative analysis in five steps

To put together a coherent, insightful analysis that goes beyond a list of pros and cons or similarities and differences, try organizing the information into these five components:

1. Frame of reference

Here is where you provide context. First, what driving idea or problem is your research anchored in? Then, for added substance, cite existing research or insights from a subject matter expert, such as a thought leader in marketing, startup growth, or investment

2. Grounds for comparison Why have you chosen to examine the two things you’re analyzing instead of focusing on two entirely different things? What are you hoping to accomplish?

3. Thesis What argument or choice are you advocating for? What will be the before and after effects of going with either decision? What do you anticipate happening with and without this approach?

For example, “If we release an AI feature for our shopping cart, we will have an edge over the rest of the market before the holiday season.” The finished comparative analysis will weigh all the pros and cons of choosing to build the new expensive AI feature including variables like how “intelligent” it will be, what it “pushes” customers to use, how much it takes off the plates of customer service etc.

Ultimately, you will gauge whether building an AI feature is the right plan for your e-commerce shop.

4. Organize the scheme Typically, there are two ways to organize a comparative analysis report. First, you can discuss everything about comparison point “A” and then go into everything about aspect “B.” Or, you alternate back and forth between points “A” and “B,” sometimes referred to as point-by-point analysis.

Using the AI feature as an example again, you could cover all the pros and cons of building the AI feature, then discuss the benefits and drawbacks of building and maintaining the feature. Or you could compare and contrast each aspect of the AI feature, one at a time. For example, a side-by-side comparison of the AI feature to shopping without it, then proceeding to another point of differentiation.

5. Connect the dots Tie it all together in a way that either confirms or disproves your hypothesis.

For instance, “Building the AI bot would allow our customer service team to save 12% on returns in Q3 while offering optimizations and savings in future strategies. However, it would also increase the product development budget by 43% in both Q1 and Q2. Our budget for product development won’t increase again until series 3 of funding is reached, so despite its potential, we will hold off building the bot until funding is secured and more opportunities and benefits can be proved effective.”

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

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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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• Comparative Analysis

What It Is and Why It's Useful

Comparative analysis asks writers to make an argument about the relationship between two or more texts. Beyond that, there's a lot of variation, but three overarching kinds of comparative analysis stand out:

• Coordinate (A ↔ B): In this kind of analysis, two (or more) texts are being read against each other in terms of a shared element, e.g., a memoir and a novel, both by Jesmyn Ward; two sets of data for the same experiment; a few op-ed responses to the same event; two YA books written in Chicago in the 2000s; a film adaption of a play; etc. 
• Subordinate (A  → B) or (B → A ): Using a theoretical text (as a "lens") to explain a case study or work of art (e.g., how Anthony Jack's The Privileged Poor can help explain divergent experiences among students at elite four-year private colleges who are coming from similar socio-economic backgrounds) or using a work of art or case study (i.e., as a "test" of) a theory's usefulness or limitations (e.g., using coverage of recent incidents of gun violence or legislation un the U.S. to confirm or question the currency of Carol Anderson's The Second ).
• Hybrid [A  → (B ↔ C)] or [(B ↔ C) → A] , i.e., using coordinate and subordinate analysis together. For example, using Jack to compare or contrast the experiences of students at elite four-year institutions with students at state universities and/or community colleges; or looking at gun culture in other countries and/or other timeframes to contextualize or generalize Anderson's main points about the role of the Second Amendment in U.S. history.

"In the wild," these three kinds of comparative analysis represent increasingly complex—and scholarly—modes of comparison. Students can of course compare two poems in terms of imagery or two data sets in terms of methods, but in each case the analysis will eventually be richer if the students have had a chance to encounter other people's ideas about how imagery or methods work. At that point, we're getting into a hybrid kind of reading (or even into research essays), especially if we start introducing different approaches to imagery or methods that are themselves being compared along with a couple (or few) poems or data sets.

Why It's Useful

In the context of a particular course, each kind of comparative analysis has its place and can be a useful step up from single-source analysis. Intellectually, comparative analysis helps overcome the "n of 1" problem that can face single-source analysis. That is, a writer drawing broad conclusions about the influence of the Iranian New Wave based on one film is relying entirely—and almost certainly too much—on that film to support those findings. In the context of even just one more film, though, the analysis is suddenly more likely to arrive at one of the best features of any comparative approach: both films will be more richly experienced than they would have been in isolation, and the themes or questions in terms of which they're being explored (here the general question of the influence of the Iranian New Wave) will arrive at conclusions that are less at-risk of oversimplification.

For scholars working in comparative fields or through comparative approaches, these features of comparative analysis animate their work. To borrow from a stock example in Western epistemology, our concept of "green" isn't based on a single encounter with something we intuit or are told is "green." Not at all. Our concept of "green" is derived from a complex set of experiences of what others say is green or what's labeled green or what seems to be something that's neither blue nor yellow but kind of both, etc. Comparative analysis essays offer us the chance to engage with that process—even if only enough to help us see where a more in-depth exploration with a higher and/or more diverse "n" might lead—and in that sense, from the standpoint of the subject matter students are exploring through writing as well the complexity of the genre of writing they're using to explore it—comparative analysis forms a bridge of sorts between single-source analysis and research essays.

Typical learning objectives for single-sources essays: formulate analytical questions and an arguable thesis, establish stakes of an argument, summarize sources accurately, choose evidence effectively, analyze evidence effectively, define key terms, organize argument logically, acknowledge and respond to counterargument, cite sources properly, and present ideas in clear prose.

Common types of comparative analysis essays and related types: two works in the same genre, two works from the same period (but in different places or in different cultures), a work adapted into a different genre or medium, two theories treating the same topic; a theory and a case study or other object, etc.

How to Teach It: Framing + Practice

Framing multi-source writing assignments (comparative analysis, research essays, multi-modal projects) is likely to overlap a great deal with "Why It's Useful" (see above), because the range of reasons why we might use these kinds of writing in academic or non-academic settings is itself the reason why they so often appear later in courses. In many courses, they're the best vehicles for exploring the complex questions that arise once we've been introduced to the course's main themes, core content, leading protagonists, and central debates.

For comparative analysis in particular, it's helpful to frame assignment's process and how it will help students successfully navigate the challenges and pitfalls presented by the genre. Ideally, this will mean students have time to identify what each text seems to be doing, take note of apparent points of connection between different texts, and start to imagine how those points of connection (or the absence thereof)

• complicates or upends their own expectations or assumptions about the texts
• complicates or refutes the expectations or assumptions about the texts presented by a scholar
• confirms and/or nuances expectations and assumptions they themselves hold or scholars have presented
• presents entirely unforeseen ways of understanding the texts

—and all with implications for the texts themselves or for the axes along which the comparative analysis took place. If students know that this is where their ideas will be heading, they'll be ready to develop those ideas and engage with the challenges that comparative analysis presents in terms of structure (See "Tips" and "Common Pitfalls" below for more on these elements of framing).

Like single-source analyses, comparative essays have several moving parts, and giving students practice here means adapting the sample sequence laid out at the " Formative Writing Assignments " page. Three areas that have already been mentioned above are worth noting:

• Gathering evidence : Depending on what your assignment is asking students to compare (or in terms of what), students will benefit greatly from structured opportunities to create inventories or data sets of the motifs, examples, trajectories, etc., shared (or not shared) by the texts they'll be comparing. See the sample exercises below for a basic example of what this might look like.
• Why it Matters: Moving beyond "x is like y but also different" or even "x is more like y than we might think at first" is what moves an essay from being "compare/contrast" to being a comparative analysis . It's also a move that can be hard to make and that will often evolve over the course of an assignment. A great way to get feedback from students about where they're at on this front? Ask them to start considering early on why their argument "matters" to different kinds of imagined audiences (while they're just gathering evidence) and again as they develop their thesis and again as they're drafting their essays. ( Cover letters , for example, are a great place to ask writers to imagine how a reader might be affected by reading an their argument.)
• Structure: Having two texts on stage at the same time can suddenly feel a lot more complicated for any writer who's used to having just one at a time. Giving students a sense of what the most common patterns (AAA / BBB, ABABAB, etc.) are likely to be can help them imagine, even if provisionally, how their argument might unfold over a series of pages. See "Tips" and "Common Pitfalls" below for more information on this front.

Sample Exercises and Links to Other Resources

• Common Pitfalls
• Advice on Timing
• Try to keep students from thinking of a proposed thesis as a commitment. Instead, help them see it as more of a hypothesis that has emerged out of readings and discussion and analytical questions and that they'll now test through an experiment, namely, writing their essay. When students see writing as part of the process of inquiry—rather than just the result—and when that process is committed to acknowledging and adapting itself to evidence, it makes writing assignments more scientific, more ethical, and more authentic. 
• Have students create an inventory of touch points between the two texts early in the process.
• Ask students to make the case—early on and at points throughout the process—for the significance of the claim they're making about the relationship between the texts they're comparing.
• For coordinate kinds of comparative analysis, a common pitfall is tied to thesis and evidence. Basically, it's a thesis that tells the reader that there are "similarities and differences" between two texts, without telling the reader why it matters that these two texts have or don't have these particular features in common. This kind of thesis is stuck at the level of description or positivism, and it's not uncommon when a writer is grappling with the complexity that can in fact accompany the "taking inventory" stage of comparative analysis. The solution is to make the "taking inventory" stage part of the process of the assignment. When this stage comes before students have formulated a thesis, that formulation is then able to emerge out of a comparative data set, rather than the data set emerging in terms of their thesis (which can lead to confirmation bias, or frequency illusion, or—just for the sake of streamlining the process of gathering evidence—cherry picking). 
• For subordinate kinds of comparative analysis , a common pitfall is tied to how much weight is given to each source. Having students apply a theory (in a "lens" essay) or weigh the pros and cons of a theory against case studies (in a "test a theory") essay can be a great way to help them explore the assumptions, implications, and real-world usefulness of theoretical approaches. The pitfall of these approaches is that they can quickly lead to the same biases we saw here above. Making sure that students know they should engage with counterevidence and counterargument, and that "lens" / "test a theory" approaches often balance each other out in any real-world application of theory is a good way to get out in front of this pitfall.
• For any kind of comparative analysis, a common pitfall is structure. Every comparative analysis asks writers to move back and forth between texts, and that can pose a number of challenges, including: what pattern the back and forth should follow and how to use transitions and other signposting to make sure readers can follow the overarching argument as the back and forth is taking place. Here's some advice from an experienced writing instructor to students about how to think about these considerations:

a quick note on STRUCTURE

     Most of us have encountered the question of whether to adopt what we might term the “A→A→A→B→B→B” structure or the “A→B→A→B→A→B” structure.  Do we make all of our points about text A before moving on to text B?  Or do we go back and forth between A and B as the essay proceeds?  As always, the answers to our questions about structure depend on our goals in the essay as a whole.  In a “similarities in spite of differences” essay, for instance, readers will need to encounter the differences between A and B before we offer them the similarities (A d →B d →A s →B s ).  If, rather than subordinating differences to similarities you are subordinating text A to text B (using A as a point of comparison that reveals B’s originality, say), you may be well served by the “A→A→A→B→B→B” structure.  

     Ultimately, you need to ask yourself how many “A→B” moves you have in you.  Is each one identical?  If so, you may wish to make the transition from A to B only once (“A→A→A→B→B→B”), because if each “A→B” move is identical, the “A→B→A→B→A→B” structure will appear to involve nothing more than directionless oscillation and repetition.  If each is increasingly complex, however—if each AB pair yields a new and progressively more complex idea about your subject—you may be well served by the “A→B→A→B→A→B” structure, because in this case it will be visible to readers as a progressively developing argument.

As we discussed in "Advice on Timing" at the page on single-source analysis, that timeline itself roughly follows the "Sample Sequence of Formative Assignments for a 'Typical' Essay" outlined under " Formative Writing Assignments, " and it spans about 5–6 steps or 2–4 weeks. 

Comparative analysis assignments have a lot of the same DNA as single-source essays, but they potentially bring more reading into play and ask students to engage in more complicated acts of analysis and synthesis during the drafting stages. With that in mind, closer to 4 weeks is probably a good baseline for many single-source analysis assignments. For sections that meet once per week, the timeline will either probably need to expand—ideally—a little past the 4-week side of things, or some of the steps will need to be combined or done asynchronously.

What It Can Build Up To

Comparative analyses can build up to other kinds of writing in a number of ways. For example:

• They can build toward other kinds of comparative analysis, e.g., student can be asked to choose an additional source to complicate their conclusions from a previous analysis, or they can be asked to revisit an analysis using a different axis of comparison, such as race instead of class. (These approaches are akin to moving from a coordinate or subordinate analysis to more of a hybrid approach.)
• They can scaffold up to research essays, which in many instances are an extension of a "hybrid comparative analysis."
• Like single-source analysis, in a course where students will take a "deep dive" into a source or topic for their capstone, they can allow students to "try on" a theoretical approach or genre or time period to see if it's indeed something they want to research more fully.
• DIY Guides for Analytical Writing Assignments

• Types of Assignments
• Unpacking the Elements of Writing Prompts
• Formative Writing Assignments
• Single-Source Analysis
• Research Essays
• Multi-Modal or Creative Projects
• Giving Feedback to Students

Assignment Decoder

Research Hypothesis In Psychology: Types, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

• A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
• It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
• A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
• Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
• For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
• Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

• Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

• Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
• However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

• Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
• Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
• Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
• Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
• Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

• The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
• The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

• Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
• Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
• Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
• Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
• Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
• Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
• Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
• Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

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How to Do Comparative Analysis in Research ( Examples )

Comparative analysis is a method that is widely used in social science . It is a method of comparing two or more items with an idea of uncovering and discovering new ideas about them. It often compares and contrasts social structures and processes around the world to grasp general patterns. Comparative analysis tries to understand the study and explain every element of data that comparing. 

We often compare and contrast in our daily life. So it is usual to compare and contrast the culture and human society. We often heard that ‘our culture is quite good than theirs’ or ‘their lifestyle is better than us’. In social science, the social scientist compares primitive, barbarian, civilized, and modern societies. They use this to understand and discover the evolutionary changes that happen to society and its people.  It is not only used to understand the evolutionary processes but also to identify the differences, changes, and connections between societies.

Most social scientists are involved in comparative analysis. Macfarlane has thought that “On account of history, the examinations are typically on schedule, in that of other sociologies, transcendently in space. The historian always takes their society and compares it with the past society, and analyzes how far they differ from each other.

The comparative method of social research is a product of 19 th -century sociology and social anthropology. Sociologists like Emile Durkheim, Herbert Spencer Max Weber used comparative analysis in their works. For example, Max Weber compares the protestant of Europe with Catholics and also compared it with other religions like Islam, Hinduism, and Confucianism.

To do a systematic comparison we need to follow different elements of the method.

1. Methods of comparison The comparison method

In social science, we can do comparisons in different ways. It is merely different based on the topic, the field of study. Like Emile Durkheim compare societies as organic solidarity and mechanical solidarity. The famous sociologist Emile Durkheim provides us with three different approaches to the comparative method. Which are;

• The first approach is to identify and select one particular society in a fixed period. And by doing that, we can identify and determine the relationship, connections and differences exist in that particular society alone. We can find their religious practices, traditions, law, norms etc.
•  The second approach is to consider and draw various societies which have common or similar characteristics that may vary in some ways. It may be we can select societies at a specific period, or we can select societies in the different periods which have common characteristics but vary in some ways. For example, we can take European and American societies (which are universally similar characteristics) in the 20 th century. And we can compare and contrast their society in terms of law, custom, tradition, etc. 
• The third approach he envisaged is to take different societies of different times that may share some similar characteristics or maybe show revolutionary changes. For example, we can compare modern and primitive societies which show us revolutionary social changes.

2 . The unit of comparison

We cannot compare every aspect of society. As we know there are so many things that we cannot compare. The very success of the compare method is the unit or the element that we select to compare. We are only able to compare things that have some attributes in common. For example, we can compare the existing family system in America with the existing family system in Europe. But we are not able to compare the food habits in china with the divorce rate in America. It is not possible. So, the next thing you to remember is to consider the unit of comparison. You have to select it with utmost care.

3. The motive of comparison

As another method of study, a comparative analysis is one among them for the social scientist. The researcher or the person who does the comparative method must know for what grounds they taking the comparative method. They have to consider the strength, limitations, weaknesses, etc. He must have to know how to do the analysis.

Steps of the comparative method

1. Setting up of a unit of comparison

As mentioned earlier, the first step is to consider and determine the unit of comparison for your study. You must consider all the dimensions of your unit. This is where you put the two things you need to compare and to properly analyze and compare it. It is not an easy step, we have to systematically and scientifically do this with proper methods and techniques. You have to build your objectives, variables and make some assumptions or ask yourself about what you need to study or make a hypothesis for your analysis.

The best casings of reference are built from explicit sources instead of your musings or perceptions. To do that you can select some attributes in the society like marriage, law, customs, norms, etc. by doing this you can easily compare and contrast the two societies that you selected for your study. You can set some questions like, is the marriage practices of Catholics are different from Protestants? Did men and women get an equal voice in their mate choice? You can set as many questions that you wanted. Because that will explore the truth about that particular topic. A comparative analysis must have these attributes to study. A social scientist who wishes to compare must develop those research questions that pop up in your mind. A study without those is not going to be a fruitful one.

2. Grounds of comparison

The grounds of comparison should be understandable for the reader. You must acknowledge why you selected these units for your comparison. For example, it is quite natural that a person who asks why you choose this what about another one? What is the reason behind choosing this particular society? If a social scientist chooses primitive Asian society and primitive Australian society for comparison, he must acknowledge the grounds of comparison to the readers. The comparison of your work must be self-explanatory without any complications.

If you choose two particular societies for your comparative analysis you must convey to the reader what are you intended to choose this and the reason for choosing that society in your analysis.

3 . Report or thesis

The main element of the comparative analysis is the thesis or the report. The report is the most important one that it must contain all your frame of reference. It must include all your research questions, objectives of your topic, the characteristics of your two units of comparison, variables in your study, and last but not least the finding and conclusion must be written down. The findings must be self-explanatory because the reader must understand to what extent did they connect and what are their differences. For example, in Emile Durkheim’s Theory of Division of Labour, he classified organic solidarity and Mechanical solidarity . In which he means primitive society as Mechanical solidarity and modern society as Organic Solidarity. Like that you have to mention what are your findings in the thesis.

4. Relationship and linking one to another

Your paper must link each point in the argument. Without that the reader does not understand the logical and rational advance in your analysis. In a comparative analysis, you need to compare the ‘x’ and ‘y’ in your paper. (x and y mean the two-unit or things in your comparison). To do that you can use likewise, similarly, on the contrary, etc. For example, if we do a comparison between primitive society and modern society we can say that; ‘in the primitive society the division of labour is based on gender and age on the contrary (or the other hand), in modern society, the division of labour is based on skill and knowledge of a person.

Demerits of comparison

Comparative analysis is not always successful. It has some limitations. The broad utilization of comparative analysis can undoubtedly cause the feeling that this technique is a solidly settled, smooth, and unproblematic method of investigation, which because of its undeniable intelligent status can produce dependable information once some specialized preconditions are met acceptably.

Perhaps the most fundamental issue here respects the independence of the unit picked for comparison. As different types of substances are gotten to be analyzed, there is frequently a fundamental and implicit supposition about their independence and a quiet propensity to disregard the mutual influences and common impacts among the units.

One more basic issue with broad ramifications concerns the decision of the units being analyzed. The primary concern is that a long way from being a guiltless as well as basic assignment, the decision of comparison units is a basic and precarious issue. The issue with this sort of comparison is that in such investigations the depictions of the cases picked for examination with the principle one will in general turn out to be unreasonably streamlined, shallow, and stylised with contorted contentions and ends as entailment.

However, a comparative analysis is as yet a strategy with exceptional benefits, essentially due to its capacity to cause us to perceive the restriction of our psyche and check against the weaknesses and hurtful results of localism and provincialism. We may anyway have something to gain from history specialists’ faltering in utilizing comparison and from their regard for the uniqueness of settings and accounts of people groups. All of the above, by doing the comparison we discover the truths the underlying and undiscovered connection, differences that exist in society.

Also Read: How to write a Sociology Analysis? Explained with Examples

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10 Powerful AI Tools for Academic Research

• Serra Ardem

AI is no longer science fiction, but a powerful ally in the academic realm. With AI by their side, researchers can free themselves from the burden of tedious tasks, and push the boundaries of knowledge. However, they must use AI carefully and ethically, as these practices introduce new considerations regarding data integrity, bias mitigation, and the preservation of academic rigor.

In this blog, we will:

• Highlight the increasing role of AI in academic research
• List 10 best AI tools for academic research, with a focus on each one’s strengths
• Share 5 best practices on how to use AI tools for academic research

Let’s dig in…

The Role of AI in Academic Research

AI tools for academic research hold immense potential, as they can analyze massive datasets and identify complex patterns. These tools can assist in generating new research questions and hypotheses, navigate mountains of academic literature to find relevant information, and automate tedious tasks like data entry.

Let’s take a look at the benefits AI tools offer for academic research:

• Supercharged literature reviews: AI can sift through vast amounts of academic literature, and pinpoint relevant studies with far greater speed and accuracy than manual searches.
• Accelerated data analysis: AI tools can rapidly analyze large datasets and uncover intricate insights that might otherwise be overlooked, or time-consuming to identify manually.
• Enhanced research quality: Helping with grammar checking, citation formatting, and data visualization, AI tools can lead to a more polished and impactful final product.
• Automation of repetitive tasks: By automating routine tasks, AI can save researchers time and effort, allowing them to focus on more intellectually demanding tasks of their research.
• Predictive modeling and forecasting: AI algorithms can develop predictive models and forecasts, aiding researchers in making informed decisions and projections in various fields.
• Cross-disciplinary collaboration: AI fosters collaboration between researchers from different disciplines by facilitating communication through shared data analysis and interpretation.

Now let’s move on to our list of 10 powerful AI tools for academic research, which you can refer to for a streamlined, refined workflow. From formulating research questions to organizing findings, these tools can offer solutions for every step of your research.

1. HyperWrite

For: hypothesis generation

HyperWrite’s Research Hypothesis Generator is perfect for students and academic researchers who want to formulate clear and concise hypotheses. All you have to do is enter your research topic and objectives into the provided fields, and then the tool will let its AI generate a testable hypothesis. You can review the generated hypothesis, make any necessary edits, and use it to guide your research process.

Pricing: You can have a limited free trial, but need to choose at least the Premium Plan for additional access. See more on pricing here .

2. Semantic Scholar

For: literature review and management

With over 200 million academic papers sourced, Semantic Scholar is one of the best AI tools for literature review. Mainly, it helps researchers to understand a paper at a glance. You can scan papers faster with the TLDRs (Too Long; Didn’t Read), or generate your own questions about the paper for the AI to answer. You can also organize papers in your own library, and get AI-powered paper recommendations for further research.

Pricing: free

For: summarizing papers

Apparently, Elicit is a huge booster as its users save up to 5 hours per week. With a database of 125 million papers, the tool will enable you to get one-sentence, abstract AI summaries, and extract details from a paper into an organized table. You can also find common themes and concepts across many papers. Keep in mind that Elicit works best with empirical domains that involve experiments and concrete results, like biomedicine and machine learning.

Pricing: Free plan offers 5,000 credits one time. See more on pricing here .

For: transcribing interviews

Supporting 125+ languages, Maestra’s interview transcription software will save you from the tedious task of manual transcription so you can dedicate more time to analyzing and interpreting your research data. Just upload your audio or video file to the tool, select the audio language, and click “Submit”. Maestra will convert your interview into text instantly, and with very high accuracy. You can always use the tool’s built-in text editor to make changes, and Maestra Teams to collaborate with fellow researchers on the transcript.

Pricing: With the “Pay As You Go” plan, you can pay for the amount of work done. See more on pricing here .

5. ATLAS.ti

For: qualitative data analysis

Whether you’re working with interview transcripts, focus group discussions, or open-ended surveys, ATLAS.ti provides a set of tools to help you extract meaningful insights from your data. You can analyze texts to uncover hidden patterns embedded in responses, or create a visualization of terms that appear most often in your research. Plus, features like sentiment analysis can identify emotional undercurrents within your data.

Pricing: Offers a variety of licenses for different purposes. See more on pricing here .

6. Power BI

For: quantitative data analysis

Microsoft’s Power BI offers AI Insights to consolidate data from various sources, analyze trends, and create interactive dashboards. One feature is “Natural Language Query”, where you can directly type your question and get quick insights about your data. Two other important features are “Anomaly Detection”, which can detect unexpected patterns, and “Decomposition Tree”, which can be utilized for root cause analysis.

Pricing: Included in a free account for Microsoft Fabric Preview. See more on pricing here .

7. Paperpal

For: writing research papers

As a popular AI writing assistant for academic papers, Paperpal is trained and built on 20+ years of scholarly knowledge. You can generate outlines, titles, abstracts, and keywords to kickstart your writing and structure your research effectively. With its ability to understand academic context, the tool can also come up with subject-specific language suggestions, and trim your paper to meet journal limits.

Pricing: Free plan offers 5 uses of AI features per day. See more on pricing here .

For: proofreading

With Scribbr’s AI Proofreader by your side, you can make your academic writing more clear and easy to read. The tool will first scan your document to catch mistakes. Then it will fix grammatical, spelling and punctuation errors while also suggesting fluency corrections. It is really easy to use (you can apply or reject corrections with 1-click), and works directly in a DOCX file.

Pricing: The free version gives a report of your issues but does not correct them. See more on pricing here .

9. Quillbot

For: detecting AI-generated content

Want to make sure your research paper does not include AI-generated content? Quillbot’s AI Detector can identify certain indicators like repetitive words, awkward phrases, and an unnatural flow. It’ll then show a percentage representing the amount of AI-generated content within your text. The tool has a very user-friendly interface, and you can have an unlimited number of checks.

10. Lateral

For: organizing documents

Lateral will help you keep everything in one place and easily find what you’re looking for. 

With auto-generated tables, you can keep track of all your findings and never lose a reference. Plus, Lateral uses its own machine learning technology (LIP API) to make content suggestions. With its “AI-Powered Concepts” feature, you can name a Concept, and the tool will recommend relevant text across all your papers.

Pricing: Free version offers 500 Page Credits one-time. See more on pricing here .

How to Use AI Tools for Research: 5 Best Practices

Before we conclude our blog, we want to list 5 best practices to adopt when using AI tools for academic research. They will ensure you’re getting the most out of AI technology in your academic pursuits while maintaining ethical standards in your work.

• Always remember that AI is an enhancer, not a replacement. While it can excel at tasks like literature review and data analysis, it cannot replicate the critical thinking and creativity that define strong research. Researchers should leverage AI for repetitive tasks, but dedicate their own expertise to interpret results and draw conclusions.
• Verify results. Don’t take AI for granted. Yes, it can be incredibly efficient, but results still require validation to prevent misleading or inaccurate results. Review them thoroughly to ensure they align with your research goals and existing knowledge in the field.
• Guard yourself against bias. AI tools for academic research are trained on existing data, which can contain social biases. You must critically evaluate the underlying assumptions used by the AI model, and ask if they are valid or relevant to your research question. You can also minimize bias by incorporating data from various sources that represent diverse perspectives and demographics.
• Embrace open science. Sharing your AI workflow and findings can inspire others, leading to innovative applications of AI tools. Open science also promotes responsible AI development in research, as it fosters transparency and collaboration among scholars.
• Stay informed about the developments in the field. AI tools for academic research are constantly evolving, and your work can benefit from the recent advancements. You can follow numerous blogs and newsletters in the area ( The Rundown AI is a great one) , join online communities, or participate in workshops and training programs. Moreover, you can connect with AI researchers whose work aligns with your research interests.

Frequently Asked Questions

Is chatgpt good for academic research.

ChatGPT can be a valuable tool for supporting your academic research, but it has limitations. You can use it for brainstorming and idea generation, identifying relevant resources, or drafting text. However, ChatGPT can’t guarantee the information it provides is entirely accurate or unbiased. In short, you can use it as a starting point, but never rely solely on its output.

Can I use AI for my thesis?

Yes, but it shouldn’t replace your own work. It can help you identify research gaps, formulate a strong thesis statement, and synthesize existing knowledge to support your argument. You can always reach out to your advisor and discuss how you plan to use AI tools for academic research .

Can AI write review articles?

AI can analyze vast amounts of information and summarize research papers much faster than humans, which can be a big time-saver in the literature review stage. Yet it can struggle with critical thinking and adding its own analysis to the review. Plus, AI-generated text can lack the originality and unique voice that a human writer brings to a review.

Can professors detect AI writing?

Yes, they can detect AI writing in several ways. Software programs like Turnitin’s AI Writing Detection can analyze text for signs of AI generation. Furthermore, experienced professors who have read many student papers can often develop a gut feeling about whether a paper was written by a human or machine. However, highly sophisticated AI may be harder to detect than more basic versions.

Can I do a PhD in artificial intelligence?

Yes, you can pursue a PhD in artificial intelligence or a related field such as computer science, machine learning, or data science. Many universities worldwide offer programs where you can delve deep into specific areas like natural language processing, computer vision, and AI ethics. Overall, pursuing a PhD in AI can lead to exciting opportunities in academia, industry research labs, and tech companies.

This blog shared 10 powerful AI tools for academic research, and highlighted each tool’s specific function and strengths. It also explained the increasing role of AI in academia, and listed 5 best practices on how to adopt AI research tools ethically.

AI tools hold potential for even greater integration and impact on research. They are likely to become more interconnected, which can lead to groundbreaking discoveries at the intersection of seemingly disparate fields. Yet, as AI becomes more powerful, ethical concerns like bias and fairness will need to be addressed. In short, AI tools for academic research should be utilized carefully, with a keen awareness of their capabilities and limitations.

About Serra Ardem

Serra Ardem is a freelance writer and editor based in Istanbul. For the last 8 years, she has been collaborating with brands and businesses to tell their unique story and develop their verbal identity.

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Open Access

Peer-reviewed

Research Article

The program efficiency of environmental and social non-governmental organizations: A comparative study

Roles Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Validation, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Faculty of Business and Management, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, Guangdong, China

• Published: May 14, 2024
• https://doi.org/10.1371/journal.pone.0302835
• Peer Review
• Reader Comments

Non-governmental organizations (NGOs) are becoming increasingly significant stakeholders in global governance and business operations. However, measuring their efficiency is a challenging task due to their mission-driven nature. While previous research has proposed financial and non-financial indicators to measure NGO efficiency, none of them has compared the differences between environmental and social NGOs. This study aims to investigate the factors influencing the program efficiency of NGOs in China and compare the differences between environmental and social NGOs. 12 indicators are employed and tested using data collected from the Chinese Research Data Services (CNRDS) platform. The study employs multiple regression analysis to examine the influential factors identified in the dataset. The findings demonstrated different influential factors of program efficiency among environmental and social NGOs. The results of the analysis provide valuable insights for NGO operators, policymakers, and researchers in the field of NGOs.

Citation: Peng S (2024) The program efficiency of environmental and social non-governmental organizations: A comparative study. PLoS ONE 19(5): e0302835. https://doi.org/10.1371/journal.pone.0302835

Editor: D. Daniel, Gadjah Mada University Faculty of Medicine, Public Health, and Nursing: Universitas Gadjah Mada Fakultas Kedokteran Kesehatan Masyarakat dan Keperawatan, INDONESIA

Received: December 17, 2023; Accepted: April 12, 2024; Published: May 14, 2024

Copyright: © 2024 Sujie Peng. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data employed in this investigation are proprietary to the CNRDS database ( https://www.cnrds.com/Home/Login ). Acquisition of the requisite access rights is feasible for any investigator through either institutional affiliation or individual registration and procurement.

Funding: This work was supported by the UIC Start-up Research Fund [grant number UICR0700043-23]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The author has declared that no competing interests exist.

Introduction

In recent times, non-governmental organizations (NGOs) have ascended to unprecedented levels of influence. According to estimates provided by the World Bank [ 1 ], the cumulative value of global transactions involving NGOs attained a remarkable $2.3 trillion in the year 2016. Nevertheless, these organizations are currently confronted with intensifying contestation for financial resources. This funding is derived from a wide array of sources, encompassing individual donors, foundations, and governmental bodies. This has brought about a sense of urgency for these organizations to be accountable and measure their performance. However, many performance reports and measurement systems tend to focus solely on financial measures, such as donations, expenditures, and operating expenses. The true success of a nonprofit should be measured by its effectiveness and efficiency in meeting the needs of its constituents, with financial considerations playing a secondary role. The act of performance measurement is deemed a pivotal strategy for NGOs to manifest their efficacy, performance, and values, with the aim of preserving public confidence and ensuring a continuous inflow of funding and resources [ 2 ]. Furthermore, it is of paramount importance for these entities to employ the most appropriate forms of evidence during the processes of development, implementation, and evaluation of their services [ 3 ].

Under the context of NGOs, efficiency can be conceptualized as meeting its mission at the lowest cost. Numerous studies, such as [ 4 , 5 ], have delved into the exploration of measures that constitute NGO efficiency. They’ve examined several indicators including the fundraising ratio, project ratio, allocative efficiency, and technical efficiency. Yet, due to the service-based nature of NGOs, efficiency measurement is still challenging [ 6 ]. In particular, measuring efficiency in the nonprofit sector is challenging due to several reasons, including the non-commercial nature of NGOs’ activities, confusing terminologies, and unsatisfactory cost-benefit ratios. The fundraising efficiency ratio, for instance, calculates the ratio between fundraising expenditures and income from donations, while the program efficiency ratio measures the percentage of resources allocated to projects related to NGOs’ missions in a period [ 7 ]. To our knowledge, in the existing works, studies regarding the assessment of NGO efficiency are lacking.

Moreover, the missions of NGOs are often complex and multifaceted, as they are shaped by the perspectives and priorities of various stakeholders involved in the organization [ 8 ]. NGOs have been categorized based on their mission focus, specifically into environmental NGOs (ENGOs), which primarily tackle environmental concerns, and social NGOs, which predominantly address societal issues. In particular, there has been a significant amount of research conducted on the influence of ENGOs as a form of informal environmental regulation on environmental quality. The literature has examined various examples, such as the role of ENGOs in environmental governance in China [ 9 ], and enhancing the ecological environment of the Baltic Sea and Adriatic-Ionian Sea regions [ 10 ]. Furthermore, NGOs play critical roles in dealing with social problems, such as poverty reduction [ 11 ] and labor rights [ 12 ]. In essence, it can launch initiatives, such as those implemented by the Rainforest Alliance [ 13 ], that enhance the social sustainability of communities by educating and integrating impoverished producers into corporate supply chains. The outcomes of these projects have led to diminished occurrences of child labor, improved financial gains for indigent producers, and increased access to employment opportunities for women. To the best of our knowledge, none of existing studies compared how the influential factors of efficiency between environmental and social NGOs.

Ragin [ 14 ] posits that the objective of comparative research is to elucidate and interpret macro-social variation. This form of research aims to pinpoint and explicate the economic, political, sociocultural, and historical parallels and/or disparities in a given phenomenon across diverse societies. As underscored by von Schnurbein et al. [ 15 ], the utilization of comparative research methodologies in the examination of NGOs has been a fundamental approach in extending our understanding of these institutions. Current literature has conducted comparative analyses at both the national and sectoral levels. For instance, Bercea et al. [ 16 ] suggest that comparative analysis can be conducted at the organizational level, allowing for a comparison between for-profit and nonprofit organizations. Similarly, Saqib et al. [ 17 ] indicate that this approach can also be applied at a national level. Comparative analysis offers benefits such as enhancing concepts, revealing specific and general forces behind a phenomenon, and creating guidelines to improve practices [ 18 ]. Thus, it’s a valuable tool for deepening our understanding of NGOs. The capacity for comparative analysis has stimulated inquiries into the catalysts behind the global expansion of NGOs, the factors enabling these organizations to adapt to diverse contexts, and the subsequent implications for our societies.

However, there’s a scarcity of studies discussing this phenomenon at the organizational level in non-western settings. On one hand, environmental nonprofits are non-government, non-commercial groups focused on issues like sustainability, wildlife protection, biodiversity management, environmental research, policy advocacy, and conservation efforts [ 19 ]. Conversely, social NGOs or socially oriented NGOs, as indicated by Kataeva et al. [ 20 ], concentrate on addressing societal challenges, including child labor, working conditions, and health and safety issues, as noted by Yawar and Seuring [ 21 ]. To my understanding, again, there is still a limited comprehension of the factors influencing the efficiencies of both types of NGOs, as well as the practical and social implications of these influences. Hence, a comparative analysis of the efficiency between environmental and social NGOs in China is necessary. Therefore, our study aims to address the following question:

• RQ 1 : What are the influential factors of NGO efficiency in China ?
• RQ 2 : How do the influential factors of program efficiency are different between environmental and social NGOs ?

The rest of this article is organized as follows: the next section reviews related literature; this is followed by a section outlining the methodology used in this study; subsequent sections present empirical results and discussion; the conclusions are presented in the final section.

Literature review

The nonprofit sector in china.

China’s nonprofit sector has grown significantly over the past forty years due to a shift from exclusivism to corporatism, now encompassing areas like environmental protection, poverty reduction, education, and healthcare. According to China’s Ministry of Civil Affairs [ 22 ], by 2016, registered nonprofits in mainland China exceeded 700,000, a 300% increase since 2000. These nonprofits employed 7.64 million full-time staff and received 78 billion RMB in donations. Furthermore, the count of unregistered nonprofits in China has also significantly risen in recent years. Estimates suggest there are over 1 million such organizations [ 23 ]. Regrettably, no precise data exists to monitor these entities.

In China, one category of NPOs is NGOs. Officially, in China, the term ’NGO’ often called ’social organization,’ includes all non-profit entities outside the state system [ 24 ]. These are independent nonprofits established to address social needs, like social services or civic organizations [ 25 ]. These NGOs are essentially non-profit organizations not managed by the government, although many are government-organized NGOs (GONGOs). Typically, these organizations are smaller and operate independently from the government. Unlike GONGOs, which depend on relationships with specific government agencies, NGOs compete for private funding to survive. This competition hinges on professional ability and accountability, not on the influence of government sponsors [ 26 ]. Official NGO registration in China requires finding a government department willing to sponsor the application. However, as NGOs often operate outside state institutions and could pose risks to sponsoring departments, few incentives exist for this task [ 27 ]. Consequently, many NGOs look for alternate registration methods to gain legal status. For instance, they might register as a for-profit entity with the State Administration for Industry and Commerce but continue their NGO operations [ 27 ]. In conclusion, the context in which NGOs operate in China differs from the Western context, yet these organizations are still striving to improve their efficiency to achieve their missions.

NGO performance measurement

Although the nature of NGOs and firms is different, the application of PM borrowed from the private sector can be extended to the context of NGOs [ 28 ]. NGOs are now expected to provide evidence of their program performance, impact, and outcomes, in addition to financial performance, to demonstrate their effectiveness in achieving their social mission. The need to measure the performance of NGOs has been driven by various factors, such as meeting the expectations of governmental agencies, donors, and other stakeholders [ 29 ]. Donors, in particular, tend to exert significant pressure on NGOs to demonstrate their performance. The measurement of performance is crucial for NGOs as it serves two main purposes: to provide evidence of the organization’s value and to benchmark performance for program and service improvement [ 29 ]. Simply put, by focusing on performance improvement and assurance, NGOs can prove their worth to donors and stakeholders, and ensure their long-term sustainability.

However, measuring the performance of NGOs can be challenging due to their social mission nature, as indicated by several scholars [ 30 , 31 ]. Their interventions’ activities and outcomes are intertwined, creating a complex relationship that makes it difficult to measure their performance. While funding or fundraising efficiency is a classic technique to measure NGOs’ performance, it does not provide a comprehensive evaluation of their financial performance. In addition to their ability to access funds, NGOs’ financial activities and their display of financial transparency, key aspects of their financial performance, should also be assessed [ 32 , 33 ].

Moreover, Polonsky and Grau [ 34 ] categorized these approaches into four groups: operating efficiency, organizational objective achievement, return on investment, and social outcomes. Efficiency in the nonprofit sector can be assessed through operating efficiency, which involves the allocation of organizational funds, including the percentage spent on social objectives [ 5 ]. Some countries mandate performance standards for NGOs, such as limiting the percentage spent on fundraising. Goal-based assessments focus on whether NPOs are achieving their social goals. NGOs can also use methods like social accounting [ 35 ] to put a dollar value on their social activities. Additionally, social outcomes are an important aspect of NGOs, where the focus is on improving social activities. Nonetheless, given that NGOs encompass a spectrum of social objectives with subjective metrics, drawing comparisons across differing issues presents a considerable challenge [ 34 ].

Efficiency measurement is a crucial aspect of organizational management as it indicates how well an organization can convert inputs into outputs. Efficiency in the context of NGOs refers to their ability to use their limited resources to achieve their goals and deliver their services effectively [ 36 ]. To clarify, efficiency in the nonprofit sector is not solely about cost reduction. Rather, it prioritizes accomplishing the organization’s mission within the constraints of available resources. This necessitates that NGOs strategically utilize their resources to fulfill their objectives, instead of merely focusing on cost-cutting. Thus, in seeking to evaluate the degree to which NGOs direct their financial resources toward the realization of their declared aims and objectives, we opted to use the proportion of financial resources expended on mission-related projects as our metric of assessment.

NGO professionalism

Numerous studies have established a positive correlation between NGO professionalism and efficiency. Selden and Sowa [ 37 ] posit that professionalizing human resources, coupled with cultivating a skilled, engaged, and paid workforce, not only elevates personnel satisfaction and service orientation but also reduces turnover and aligns with organizational objectives, thereby driving productivity. Similarly, drawing from Kreutzer and Jäger [ 38 ], heightened professionalization, characterized by a focus on efficiency, fundraising, formalization, control, and reporting, is expected to enhance revenue generation and cost reduction. Consequently, this could decrease the cost per output or asset-per-beneficiary ratio, and improve the labor productivity indicator, defined as revenue per unit of human resources. Also, as per Ni et al. [ 39 ], professionalization generally boosts fundraising efficiencies in private foundations, particularly when raising unrestricted funds, a trend not observed in public foundations.

Conversely, a different set of studies has discovered a negative correlation between NGO professionalism and efficiency. With an escalation in professionalization, there is a greater propensity for the managerial persona to supersede the volunteer persona. While the volunteer persona is anchored in an altruistic paradigm, emphasizing democratic engagement and the accomplishment of the organization’s mission, the managerial persona adheres to a paradigm of formalization, specialization, and efficiency [ 38 ]. Therefore, two potential adverse outcomes of professionalization within NPOs include goal displacement, which is linked to the self-interest logic prevalent in for-profit entities [ 40 ], and the surfacing of conflicts due to formalization’s significant negative influence on volunteer motivation [ 38 ]. Different from the above-mentioned studies, Sanzo-Perez et al. [ 41 ] substantiate a ’U-shaped’ association between professionalization and an NPO’s capacity to cater to a larger beneficiary base with fewer resources. Furthermore, professionalization exerts a positive impact on revenue generation, and collaborations with commercial entities augment the non-profit’s resource-to-beneficiary ratio.

NGO political connections

As previously noted, the establishment of political ties between Chinese NGOs and the State engenders a multitude of organizational advantages. These advantages encompass enhanced access to information, regulatory leniency, and bolstered economic performance [ 42 ]. Chinese NGOs exhibit strategic acumen in managing their relationships with the State, employing a diverse array of approaches to cultivate and sustain political guanxi. Despite the legal requirement for NGOs to possess a sponsoring government agency, they frequently foster relationships that extend beyond the stipulated mandate [ 43 ]. A prevalent strategy employed by NGOs to instigate relationships with the State involves inviting former high-ranking government officials to join their ranks, serving in capacities such as board members, presidents, honorary presidents, or part-time staff members [ 44 ]. Lu [ 45 ] characterizes this strategy as particularly potent, given that the management of NGOs in China is predicated on the "rule of men" (renzhi), as opposed to the "rule of law" (fazhi). NGOs leverage their political connections to broaden the scope of activities they can undertake, surpassing the constraints imposed by existing legal regulations [ 43 ].

Hypothesis development

Ngo professionalism and program efficiency.

Nonprofits are staffed by both paid employees and volunteers, leading to questions about organizational identity and culture [ 41 ]. The degree of professionalization within nonprofits varies, but many organizations have increased staffing and resources to keep up with the competitive environment [ 46 ]. From the knowledge-based perspective, knowledge is the only resource that provides a sustainable competitive advantage [ 47 ]. Professionalization examines the extent to which an NGO has a division of labor and the specification of responsibilities and positions (Hwang and Powell, 2009). In summary, the concept of professionalization within organizations can be interpreted as the acquisition and refinement of skills among the workforce.

Under the context of NGOs, professionalization can be reflected by the increasing recruitment of full-time employees [ 48 ]. For example, the possibility of securing government funding can be improved when nonprofits recruit fewer volunteers [ 49 ]. However, in the existing works, the impact of the professionalization of human resources on NGO efficiency is mixed.

On one hand, Striebing [ 50 ] asserts that the principal driving force for voluntary transparency in organizations is professional management, such as the hiring of full-time personnel, which leads to higher overall efficiency [ 37 ]. Yet, on the other hand, the pursuit of professionalization may cause the ignorance of volunteers within NGOs [ 38 ]. In other words, the unbalanced relationship between paid staff and volunteers may lead to ineffective human resource management within the nonprofits. Differing from these works, nonlinear relationships can be also identified. For instance, Sanzo-Perez et al. [ 41 ] pointed out a nonlinear relationship between professionalization (e.g., professionalized human resources) and a nonprofit organization’s ability to serve more beneficiaries with fewer assets, showing a U-shaped curve.

In a similar vein, Suárez [ 49 ] also pointed out that professionalized organizational structure design can improve NGOs’ possibility of securing funding provided by governmental agencies. It provides organizations with knowledge for successful project execution [ 51 ].

Once again, more professionalized NGOs are more likely to adopt an effective internal governance mechanism (e.g. efficient organizational structure) and mobilize external resources (e.g. qualified staff with extensive social connections). In essence, professionalization not only enhances NGOs’ governance but also fosters balanced stakeholder relationships and minimizes conflicts of interest, facilitating collaborative projects for improved program efficiency. Therefore, the following hypothesis is developed:

• H1 : NGO professionalism is positively associated with efficiency;
• H1a : ENGOs’ professionalism is positively associated with their program efficiency;
• H1b : Social NGOs’ professionalism is positively associated with their program efficiency;

Political connection and program efficiency

In many studies, organizational performance relies on not only organizations’ internal resources but also their social networks. In the context of business studies, many studies have found a positive association between the quality of social capital and firm performance. Specifically, the network can comprise political ties, such as informal connections with government officials and organizations [ 52 ], or business ties, like informal connections with other business organizations [ 53 ]. Not coincidentally, a positive linkage can be found between having connections with former politicians and firm performance [ 54 ]. However, according to Wong and Hooy [ 54 ], firm performance positively correlates only with stable political connections, such as those with government-linked companies and boards of directors, but not with less stable connections like those with businesspeople or family members. In other words, firms are more likely to operate effectively by cultivating boards whose members have political experience and by recruiting directors who also have strong political experiences [ 55 ]. More recently, Lee [ 56 ] investigated the operations of Chinese family businesses and found that the connections between families and other firms are more likely to enhance firms’ networks and performances. However, political connections negatively impact the performance of family businesses [ 56 ]. In particular, especially in the Chinese context, scholars have identified the impacts of political connections on organizational performance. The predominant understanding is that, in the not-for-profit sector, political ties can contribute to program efficiency.

In our context, political alignment makes positive impacts on NGOs’ efficiency. Indeed, as Ni and Zhan [ 57 ] established, nonprofits with strong political ties have an advantage in securing government subsidies, considering the government’s extensive influence over crucial decisions such as resource allocation, information access, and legal protection. Moreover, as previously mentioned, increased financial resources enable NGOs to hire professionals and external consultants to enhance their internal governance, thereby improving the quality of their services. Concurrently, political connections offer NGOs a more stable environment and resources for their activities. Based on these premises, the following hypothesis is proposed:

• H2 : With stronger political connections , NGOs are more efficient;
• H2a : With stronger political connections , ENGOs are more efficient;
• H2b : With stronger political connections , social NGOs are more efficient;

Methodology

This study aimed to address the aforementioned issue through the use of datasets obtained from a research database. The following section provides a rationale for the methodology employed in this study.

In this section, I explicate the reasoning for the selection of variables in my study. I employed a total of 12 variables, grouped into four categories: (1) NGO professionalism, (2) political connections, (3) control variables, and (4) NGO efficiency. A detailed description and categorization of these variables can be found in Table 1 .

• PPT PowerPoint slide
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https://doi.org/10.1371/journal.pone.0302835.t001

As shown in the three panels of Table 1 , this study utilized 12 variables. Generally, all of the variables were chosen in accordance with guidelines set in previous studies. For instance, based on Coupet and Berrett [ 58 ]and Harris et al. [ 59 ], Efficiency , represented by the percentage of funds that NGOs allocate for mission-related purposes, serves as an independent variable to gauge NGO efficiency.

The dependent variables, as previously stated, encompass NGO professionalism and political affiliations. The measurement of NGO political connection aims to capture the extent to which NGOs have developed strong networks with government agencies. In the context of this study, based on Johnson and Ni [ 60 ], it is measured by four indicators: " Registration " (NGO registration level), " Previous_Gov " (number of staff who have worked in provincial governmental organizations), " Current_Gov " (number of staff who are currently in national governmental agencies), and Gov_Fund (NGOs’ total funds from the government). Finally, the control variables were selected based on Wang [ 61 ]. In this sense, the following variables were selected in this study: Yr (number of years since NGOs started operations), Scope (whether NGO is operating nationwide or locally), and Start_Fund (NGO startup capital).

Data collection and sampling

The present study utilized data obtained from the Chinese Research Data Services Platform (CNRDS), a reputable database that offers high-quality quantitative data for research purposes. Specifically, the study relied on the Chinese NGO (CNGO) dataset, which consists of 16 datasets containing various information related to NGOs, including financial reports and statistics on stakeholders and donations. After eliminating missing data, the study focused on data collected in 2016 for subsequent analysis.

The dataset used in the study comprises around 60 variables, including the number of donations (Donrev) and the position of the management board members in NGOs (Pos). This dataset is unique in that it also includes variables such as the characteristics of NGOs’ stakeholders and long-term equity investments, which can offer new and valuable insights into the analysis. The initial dataset for this study comprised 6,589 samples and was obtained from the CNRDS, a high-quality research database that provides quantitative data. Specifically, the study utilized the CNGO dataset within the CNRDS, which consists of 16 datasets, including data from NGOs’ financial reports, statistics on stakeholders and donations, and other related variables. After excluding samples with missing data, the data in 2016 was selected for further analysis. However, the raw dataset required cleaning due to various issues. A total of 402 samples were excessive, 1,776 contained missing data, 2,287 were problematic statistics, and the remaining samples lacked complete records. Consequently, 2,124 samples were deemed suitable for subsequent analysis. Adopting Momin’s [ 62 ] classification, social NGOs advocate for human rights, poverty reduction, and other social welfare issues, while ENGOs focus on influencing policies related to environmental pollution. In the current research, a comprehensive analysis was carried out that included 146 ENGOs and 1,978 social NGOs.

Data analysis

Regression analysis..

Empirical results. Table 2 presents the descriptive statistics of the variables used in the study before discussing the empirical results.

https://doi.org/10.1371/journal.pone.0302835.t002

In particular, the level of governmental organization that granted the NGO license (Registeration), the number of staff members who have previously worked for the government (Pre_Gov), and the number of staff members who are currently working for the government (Current_Gov) were used to measure NGOs’ political ties. The results suggest that within the NGOs sampled, an average of 0.08 and 0.35 of the staff have experience working either previously or currently for the government, respectively.

As previously mentioned, the professionalism of NGOs is assessed using various indicators such as the years since the organization’s establishment (Yr), government evaluation of NGOs ( Feedback ), number of full-time staff ( Staff ), number of volunteers ( Volunteer ), and the regional scope in which NGOs operate ( Scope ). The descriptive statistics show that the average operation period for the sample NGOs is 11.51 years. Regarding government evaluation, most NGOs have a rating of 1.32A, with only a few certified with a 5A rating. The majority of NGOs have more than three full-time staff and 85 volunteers.

Besides, the descriptive statistics and correlations are presented in Table 3 . A correlation analysis was conducted to examine the relationship between the dependent and independent variables, and the results were presented in Table 3 . It can be seen that the number of full-time staff, amount of government fund and annual income are statistically and negatively correlated with NGO program efficiency.

https://doi.org/10.1371/journal.pone.0302835.t003

In addition, the results of the multicollinearity test are illustrated in Table 4 . When building a regression model, it is important to test for the multicollinearity of variables, as highlighted by Robinson and Schumacker [ 64 ]. Multicollinearity can lead to inflated variances of the variables, which can result in problematic regressions due to the limited new and independent information that the variables can provide Daoud [ 65 ]. To detect the degree of multicollinearity, the variance inflation factor (VIF) is often used, which measures the degree of correlation of an independent variable with the other independent variables [ 65 – 67 ]. Daoud [ 65 ] recommends using a VIF of 5 as the threshold for excessive multicollinearity. The findings indicate that all variables exhibited a VIF value below 3, thereby implying an absence of severe multicollinearity within the regression models.

https://doi.org/10.1371/journal.pone.0302835.t004

Table 5 delineates the regression analysis outcomes for ENGOs, thereby elucidating a positive and statistically significant correlation between the number of full-time staff and the efficiency of the NGO. Concurrently, it also indicates a negative and significant association between their annual revenue and program efficiency. The inverse correlation between annual revenue, the quantity of full-time employees, and operational efficiency is also observable in the realm of social NGOs. It is of considerable importance to note that performance feedback plays a pivotal role in enhancing the efficiency of such social NGOs. Considering the findings from Model 2, the regression analysis unveiled an inverse relationship between the efficiency of both environmental and social NGOs and the magnitude of government funding they procure. In contrast to ENGOs, there is a significant and negative association between the efficiency of social NGOs and their level of registration. Within the context of social NGOs, those with superior registration levels (for instance, registered with national governmental agencies) demonstrate greater efficiency when contrasted with entities possessing lower registration statuses (such as those registered with municipal governmental agencies).

https://doi.org/10.1371/journal.pone.0302835.t005

In Table 6 , the robustness test of the empirical results is presented. Based on Table 6 , the results show that all outcomes are robust as one variable was replaced by another alternative variable in each model. Particularly, in models 1 and 2, the variable Scope which represents the regional scope for NGO’s operation, was replaced by the variable Staff which indicates the number of full-time staff. As a result, it was found that the replacement of two variables in models 1 and 2 did not significantly alter the significance of the models. This indicates that the regression models are robust and the results are reliable.

https://doi.org/10.1371/journal.pone.0302835.t006

Within business literature, research has been conducted comparing performance indicators across enterprises with varying ownership structures. For example, Lazzarini and Musacchio [ 68 ] analysis shows that state-owned enterprises (SOEs) perform similar to private firms, except when facing shocks like severe recessions that emphasize their social and political goals. Organizations with larger government stakes tend to emphasize governmental duty indicators over ethical and economic ones in their Performance Measurement Systems and compensation, while non-SOEs focus more on economic value indicators, neglecting societal measures [ 68 , 69 ]. In summary, these studies highlight the varying impacts of different ownership structures on the key determinants of organizational performance. In this study, it was assumed that the factors influencing performance may differ among nonprofits, based on their mission focuses. Echoing Momin’s [ 62 ] classification, social NGOs work towards human rights, poverty reduction, and other social welfare matters, while ENGOs concentrate on impacting policies pertaining to environmental pollution. Following these, the subsequent discussions are formulated.

In terms of the impact of NGO professionalism on efficiency, the initial regression model demonstrated a correlation between NGO professionalism and efficiency. The analysis suggests that positive feedback marginally enhances the efficiency of environmental and social NGOs. Theoretically, Baker et al. [ 70 ] conceptualized feedback as a continuous, interactive communication process conveying information about an individual’s performance in work-related tasks. A multitude of studies have demonstrated that a discrepancy between an organization’s actual and aspired performance—manifested in areas such as the intensity of research and development [ 71 ], the rate of new product introductions [ 72 ], and the extent of strategic changes [ 73 ]—can emerge when their performance surpasses targeted aspirations. Under the context of organizational studies, Ref and Shapira [ 74 ] illustrate that firms, when significantly under or over their aspiration level, show a marked change in behavior, reflected in an inverted U-shaped relationship with their likelihood to enter new markets. Besides, Kotiloglu et al. [ 75 ] posit that performance feedback influences investment and growth, strategic alterations, R&D intensity, and organizational risk-taking, but it does not catalyze product innovation. The results of our regression statistics are consistent with these notions.

In addition to performance feedback, we also assumed that NGOs with more full-time staff are more efficient. In NGOs, human resources primarily consist of full-time employees and volunteers. Yet, they are playing different roles in NGOs. According to Bowman [ 76 ], nonprofit managers should not view volunteers as substitutes for paid staff. Ariza-Montes et al. [ 77 ] posit that paid staff, due to their professional training, hold positions of responsibility overseeing complex activities, while volunteers, determined by their availability and commitment, contribute to various tasks within a flexible schedule. Put simply, while volunteers provide valuable manpower in the nonprofit sector, their proficiency in handling everyday tasks can be limited. Therefore, it’s arguable that full-time NGO staff are essential for boosting organizational performance. Nevertheless, the findings reveal a discrepancy in the correlation between the size of paid staff and efficiency across different NGO types. Specifically, while a positive correlation is observed between the size of paid staff and efficiency in ENGOs, a negative correlation emerges in the context of social NGOs. This divergence could potentially be attributed to the fact that an increased staff size may diminish staff participation in decision-making [ 78 ], a factor that may exert a negative influence in social-oriented nonprofits. Ecer et al. [ 7 ] delineate a positive correlation between a nonprofit organization’s reliance on donations and its effectiveness. Yet, our findings indicate a significant negative correlation between the income of NGOs and their efficiency, with this association being notably stronger in ENGOs as compared to their social counterparts. One of the reasons might be that organizations may face pressure to allocate resources to building reserves for future repair and replacement needs, potentially hindering current performance [ 79 ].

Moreover, based on hypothesis 2, there exists a substantial body of literature in organizational studies that explores the impacts of political connections. Through empirical investigation, it was determined that political involvement can enable nonprofit foundations to secure additional government subsidies, attract more donations, and increase market revenue [ 57 ]. Similarly, Zhan and Tang [ 80 ] posit that ENGOs, when led by current government officials or legislative body members and maintaining robust guanxi, are likely to experience increased funding stability, a more sophisticated management system, and a higher propensity for policy advocacy. Based on the regression outputs, political connections are negatively related to NGO efficiency. Indeed, Coupet [ 81 ] suggested that government funding is a crucial input that can enhance organizational stability and aid in achieving significant social objectives. However, it can also increase costs to meet the requirements imposed by government agencies, potentially leading to a loss of uniqueness due to conforming to the guidelines provided by the government. In a similar vein, Jobome’s [ 82 ] study found a positive correlation between government funding and governance requirements, as well as traditional charity structures, with efficiency. However, the adoption of business-type corporate governance codes does not show a similar relationship. In line with prior works, based on the regression statistics, it can be seen that social NGOs’ efficiency is slightly and negatively influenced by the number of grants from government agencies. The reasons might be that obtaining government funding can be time-consuming [ 83 ] and such funding is often earmarked for particular uses [ 84 ]. Specifically, the level of government financial aid to NGOs is contingent upon the nature of their missions. According to Xie et al. [ 85 ], under the "big government, small society" program, Chinese ENGOs face an uneven distribution of government support, with sectors like nuclear security and the green economy finding it particularly challenging to secure funding. To put it differently, NGOs working on sensitive issues (e.g., social issues) that could be perceived as potential threats to the state may be more prone to restrictions imposed by the political system. Despite NGOs’ positive impact, the government has curbed their growth [ 86 ]. They’ve developed legal tools to manage NGOs, viewing them as potential threats [ 87 ]. Thus, the inverse relationship between government funding and program efficiency is only seen in NGOs focused on social issues.

Additionally, Assenova and Sorenson [ 88 ] contend that legal registration is a crucial activity as it alters the legal status of a new business venture and bestows it with sociopolitical legitimacy, which in turn, enhances the venture’s access to various resources such as financial capital, human capital, raw materials, and customers. Relating this to our context, it can be seen from the results that social NGOs are more efficient when they register with senior government agencies. Such a linkage cannot be found in ENGOs. One of the reasons might be that, compared with ENGOs, the current registration system curtails the autonomy of NGOs, affecting their finances, personnel, and decision-making [ 89 ]. Chinese people may distrust NGOs because they represent special interests, which citizens regard as selfish [ 90 ]. However, the impact of this situation can be mitigated through the use of political connections. Indeed, in China, political trust significantly bolsters social trust [ 91 ].

NGOs primarily engage with state agencies to gain public trust, which Farid and Song [ 92 ] identified as essential for these groups to effectively operate their programs. In this sense, Johnson and Ni [ 60 ] found a modest yet positive correlation between the level of government funding received by an NGO (an indicator of legitimacy) and the amount of private donations it attracts. In terms of NGO project implication, Hildebrandt [ 43 ] suggests that NGOs leverage their political connections to expand their scope of activities beyond what is permitted by existing legal regulations. This study further reveals that the positive correlation between dual registration and NGO program efficiency is especially pronounced among social NGOs.

Moreover, in China, the non-profit sector is still relatively nascent, leading to an uncertain and fluid relationship between the state and the sector (Kang, 2017). This relationship is also dynamic and multifaceted [ 93 ], operating under frequently ambiguous political guidelines [ 89 ]. Indeed, China’s NGO registration system created significant difficulties for NGOs’ formation and operation. NGOs that managed to register under this dual management requirement were barred from creating local branches and had to undergo rigorous annual government reviews and reporting duties. Furthermore, the government employed subtle and indirect methods to regulate and control NGOs, such as offering funding or training, mandating the formation of communist party units within NGOs, and withholding essential resources [ 87 ]. Hence, registered NGOs have fostered self-censorship, focusing on obtaining government information and orderly participation [ 94 ]. They avoid actions that could be seen as challenging the government or threatening regime stability [ 95 ]. In addition, despite a corporatist regulatory system imposing strict registration and representational monopolies, Chinese ENGOs manage to function effectively due to self-regulation, international state pressure, and strong state-media relationships [ 96 ]. This could explain why a significant negative correlation among ENGOs cannot be observed in our context.

This study aimed to investigate the determinants of NGO efficiency by analyzing the differences between environmental and social NGOs. Generally, it can be observed that there are differences in the influential factors of efficiency between environmental and social NGOs. Specifically, the regression analysis of ENGOs showed a positive and significant impact of the number of full-time staff on efficiency, while a negative and significant relationship was found between annual income and efficiency. Similar negative relationships between annual income, full-time staff number, and efficiency were also observed in social NGOs. Additionally, performance feedback was found to be a significant factor for social NGOs. The regression analysis of model 2 showed a negative correlation between the efficiency of both environmental and social NGOs and the amount of government funds they receive. However, in contrast to social NGOs, the efficiency of ENGOs was negatively and significantly associated with their registration level.

This study has made several significant contributions. Firstly, it has identified the influential factors of NGO efficiency and highlighted the differences between environmental and social NGOs, which contributed to the existing literature. Secondly, in terms of practical and social contributions, this study has provided valuable insights for NGO managers and policymakers in China to develop tailored measures to enhance efficiency for social and ENGOs (e.g., to improve program efficiency, compared with ENGOs, engagement of full-time staff in social NGOs). These findings have important implications for the development of the NGO sector in China and can guide policymakers in the field.

The present study is not without limitations. Firstly, the study is based on a secondary dataset obtained from the CNRDS database. As the data was collected in 2016, it may not reflect more recent developments or the current context, and thus could be considered somewhat outdated. For instance, the study does not consider the impact of the Covid-19 pandemic.

The second limitation is related to the complexities inherent to any attempt at quantifying political connections and their impacts. No universal consensus has yet been reached as to how such idiosyncratic social phenomena ought to be measured, and indeed, scholars such as Wang et al. [ 97 ] have all published studies that apply divergent indicators in pursuit of this elusive variable. The as-yet imperfect definition of political ties and their influences–a key variable in any analysis within this field–must be acknowledged. Future research in this vein must build upon previous models and seek to refine the definition of this crucial factor.

The third limitation concerns government feedback. As numerous researchers have recognised, feedback comes in many different forms, and its impacts can be just as diverse. For example, Swift and Peterson [ 98 ] propose that negative feedback can enhance motivation for conscientious and neurotic individuals during playful tasks, but conversely, it can hinder their motivation when tasks are frustrating. Thus, different types of feedback (e.g., positive and negative feedback) can lead to different consequences, which our model failed to account for. In this regard, future research should consider the impacts of different types of government feedback (e.g., positive and negative feedback) on NGO program efficiency.

• View Article
• Google Scholar
• PubMed/NCBI
• 14. Ragin CC. The comparative method: Moving beyond qualitative and quantitative strategies. Univ of California Press; 2014 Jul 18.
• 16. Bercea OB, Lakatos ES, Bacali L. Comparative Study Regarding Organizational Culture: Nonprofit Organization and Profit-Oriented Organization. InCivil Society: The Engine for Economic and Social Well-Being: The 2017 Griffiths School of Management and IT Annual Conference on Business, Entrepreneurship and Ethics (GMSAC) 2019 (pp. 41–52). Springer International Publishing.
• 29. Fowler A. Assessing NGO performance: difficulties, dilemmas and a way ahead. InNon-Governmental Organisations-Performance and Accountability 2014 Mar 18 (pp. 143–156). Routledge.
• 30. Drucker P. Managing the non-profit organization. Routledge; 2012 Oct 12.
• 43. Hildebrandt T. Social organizations and the authoritarian state in China. Cambridge University Press; 2013 Feb 18.
• 90. Shi T. China: Democratic values supporting an authoritarian system. InHow east Asians view democracy 2008 Dec 31 (pp. 209–237). Columbia University Press.
• 93. Shieh S. Beyond corporatism and civil society: Three modes of state–NGO interaction in China. InState and society responses to social welfare needs in China 2009 Jun 2 (pp. 38–58). Routledge.

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Formulating Appropriate Statistical Hypotheses for Treatment Comparison in Clinical Trial Design and Analysis

1 Johns Hopkins University

2 Guangdong Provincial Hospital of Traditional Chinese Medicine

Steven Piantadosi

3 Cedars Sinai Medical Center

4 Georgetown University

Ideally designed null and alternative hypotheses should correspond to a partition of all possible true probability models such that the alternative hypothesis can be inferred upon the rejection of null hypothesis. However, tests are often carried out and recommendations are made without a precise specification of alternative hypothesis. This can lead to different superiority claims depending on which test is used instead of scientific plausibility. Such differences are often overlooked. We provide relationship of different superiority specifications in typical hypothesis testing. This can help investigators to select proper hypotheses for treatment comparison in clinical trial design.

1. INTRODUCTION

2. STATISTICAL FRAMEWORK

As a real data example, we have collected survival times from 207 patients who all died of prostate cancer ([ 9 ], see Figure 1 ). The Kaplan-Meier estimates of overall survival suggest that, for most part of time interval, group 1 has longer survival than group 2. The two curves cross simply because 3 patients in group 2 had much longer survival time than anyone else. However, the logrank test yields p -value = 0.13. The R 2 = 0.028 from the Cox model shows a poor fit under the proportional hazards assumption. On the other hand, Wilcoxon test and t -test of log survival time give p -values of 0.007 and 0.01 respectively, suggesting group 1 has significantly longer overall survival time than group 2. If the null hypothesis is rejected by the logrank test, the alternative hypothesis H a 1 = { P: h 1 ( t )/ h 2 ( t ) = HR < 1, ∀ t > 0} is implicit. If the Wilcoxon test is used, the alternative hypothesis H a 2 = { P: P { X < Y } − P { X > Y } > 0} is implicit. Here X is the survival time of group 1, and Y is the survival time of group 2. Interpretation of θ = P { X < Y } − P { X > Y } is given in the next section. The logrank test assumes the hazard ratio does not change over time. This is a much stronger claim than θ > 0 that leads to H a 1 ⊆ H a 2 . This example shows that a survival benefit from group 1 can be claimed through a weaker claim “ θ > 0” but not through a stronger claim of hazard ratio “ HR < 1”.

Overall survival

Not all claims of superiority are nested. In such cases, careful selection of the alternative hypothesis is necessary to support a robust study conclusion. For example, if both random variables X and Y are survival times having Weibull density f ( x ) = 1 2 x e - x and g ( y ) = e − y respectively, then Y has longer median survival time than X but with shorter mean survival time. If the alternative hypothesis states that Y has longer mean survival time than X , it is less likely to be supported by the data. Increasing sample size does not help to increase power to launch such a claim from the alternative hypothesis. In fact, a larger sample size is more likely to show no increase in population mean. Even if the null hypothesis is rejected by the current data with p < 0.05 and a one-sided alternative is claimed (i.e., a type I error is encountered), this finding is unlikely to reproduce in future or in larger studies.

Although different claims can be used to define superiority, it would be helpful for investigators to know how they are related when formulating hypotheses for their studies. This paper presents relationships among 5 commonly used claims to define superiority through population means, medians, stochastic ordering, and relative treatment effect. We provide the relative strength of each claim, conditions needed for one claim to imply another one, and conditions for two claims to be equivalent. Examples to illustrate these relationships are presented. We provide suggestions how to select a claim for the alternative hypothesis to reflect the underlying biological properties and reach a robust conclusion.

The rest of the paper is arranged as follows. Section 3 gives precise statements of 5 commonly used claims to define superiority in alternative hypotheses and how they arise in practice. Section 4 gives theoretical relationships and examples of the 5 claims. Proofs of all theorems are provided in the Appendix . We conclude the paper with some recommendations.

3. FIVE CLAIMS TO DEFINE SUPERIORITY IN ALTERNATIVE HYPOTHESES

Without a loss of generality, we assume that larger observations correspond to a better clinical outcomes. If we use X and Y to denote outcomes from treatments T X and T Y respectively, we say that Y is stochastically larger than X (denoted as Y ≻ X ) if P { Y > t } ≥ P { X > t } for all t , and there exists at least one t 0 such that P { Y > t 0 } > P { X > t 0 }. A simple way to interpret stochastic ordering is that, for any cutoff point t , treatment T Y is more likely to yield observations greater than t than treatment T X is. The 5 claims to be compared in this paper are:

• (c1) Y is stochastically larger than X , denoted as Y ≻ X ;
• (c2) the mean of treatment T Y is greater than the mean of treatment T X , denoted as μ Y > μ X ;
• (C3) the median of treatment difference ( Y − X ) is greater than zero, denoted as med ( Y − X ) > 0;
• (C4) θ = P { X < Y } − P { X > Y } > 0;
• (C5) the median of treatment T Y is greater than the median of treatment T X , denoted as med Y > med X .

The θ defined in (c4) is a value between −1 and +1 that measures how likely that Y gives a greater value than X . When θ = −1, Y is 0% likely to be greater than X ; When θ = +1, Y is 100% likely to be greater than X . When θ > 0, Y has larger probability to be greater than X , and when θ < 0, Y has smaller probability to be greater than X .

Parameter θ was called the “relative treatment effect” by Brunner, Munzel, and Puri ([ 10 ]), and was further extended by Huang et al. ([ 11 ], [ 12 ], [ 13 ]) to define “global treatment effect” when comparing multiple outcomes. The Wilcoxon-Mann-Whitney rank test is sometimes referred to as a test of medians. It is better viewed as a test for relative treatment effect θ . Examples of using claim (c1) to define better include: the hazards ratio of treatment T Y versus treatment T X is less than 1; the odds ratio of T Y versus T X is greater than 1; and positive treatment effect in a number of regression models where treatment benefit is defined through a location shift: P { Y ≤ t } = P { X ≤ t − δ } for some δ > 0. When outcome measures are non-negative, such as tumor size, a scale shift model P { Y ≤ t } = P { X ≤ t / β } for some β > 1 is another example of stochastic ordering in ( c 1). When random variables X and Y are assumed to have distributions in the same monotone likelihood ratio family, such as when a likelihood ratio test is used, the stochastic ordering assumption is implicitly made. Claim ( c 2) is most frequently used in clinical trials to define improvement. Claim ( c 3) is sometimes used when differences between matched pairs are of interest. Examples include studies on how tumor size changes before and after neoadjuvant therapy from the same patient, difference between treated and untreated eyes from the same subject, and similarly in matched case-control studies. Claim ( c 4) is often used when performing nonparametric tests such as the Wilcoxon rank test. If data have a continuous distribution, claim θ > 0 is equivalent to the claim of p = P { Y > X } > 1/2 because θ = 2 p − 1. Claim ( c 4) is often chosen when investigators have little knowledge about outcome distributions. Claim ( c 5) is often used when data are highly skewed or when quantiles are of interest. For example, comparison of median survival between two treatments is performed in cancer studies. ( c 5) is also used in sign test and quantile regression.

4. RELATIONSHIP AMONG CLAIMS ( c 1) – ( c 5)

We investigate claims ( c 1) – ( c 5) by showing whether one is nested within another, and whether and when two claims are equivalent. Let X and Y be two random variables denoting the outcomes from treatments T X and T Y respectively. Their corresponding cumulative distribution functions are F ( x ) and G ( y ). We use Serfling’s ([ 14 ]) method to define the u th quantile q ( u ) of a cumulative distribution F :

The median is defined as the 0.5th quantile. Notations med X , med Y and med Y − X are used to denote the medians of X , Y and ( Y − X ) in claims ( c 3) and ( c 5) respectively. Theorem 1 below shows that claims ( c 1) and ( c 2) are always nested, claims ( c 1) and ( c 4) are nested if two treatments do not give the same result, and claims ( c 1) is stronger than claims ( c 2), ( c 3) and ( c 4) for data with continuous distributions.

• Claim ( c 1) is always stronger than claim ( c 2);
• Claim ( c 1) is stronger than claims ( c 3) and ( c 4) if the probability that the two treatments give the same outcome is zero;
• Claim ( c 1) is stronger than all other 4 claims ( c 2 through c 5) if data from both treatment groups have continuous distributions and P { Y > t } > P { X > t } for all t .

When both X and Y are continuous random variables, the condition P ( X = Y ) = 0 is naturally satisfied. Thus, claim ( c 1) is stronger than claim ( c 4) for all continuous endpoints. The reverse is generally not true: claim ( c 2) or ( c 4) do not necessarily imply claim ( c 1). The prostate cancer data described in the introduction demonstrates this. Under the same α = 0.05 significance level (type I error), the stochastic ordering defined by a hazard ratio cannot be claimed by the logrank test even though both ( c 2) and ( c 4) can be claimed by the t -test and Wilcoxon test respectively. Clearly, claim ( c 1 * ) = P { Y > t } > P { X > t } for all t is stronger than claim ( c 1) [i.e., ( c 1 * ) is a subset of (c1)]. Claim ( c 1 * ) is frequently seen in location shift linear regression models for continuous response variables where the binary indicator of treatment assignment is included as a linear covariate. In fact, most examples of ( c 1) given in previous sections actually belong to ( c 1 * ). To see this, examine the location shift model P { Y ≤ t } = P { X ≤ t − δ } for two random variables X and Y with normal distributions. Apparently, ( c 1 * ) is true when δ > 0. An example of ( c 1) that does not belong to ( c 1 * ) is when the two distribution function curves meet. For example, when event times from all patients are observed in a finite time interval [0, T ]. Then the survival functions (or distribution functions) between the two treatment groups are the same in time interval ( T , ∞).

Claim ( c 4) is typically concluded following a nonparametric test while claim ( c 2) is concluded more often after a parametric test or a semiparametric test. It is natural to guess that they are not related. In general, neither is stronger than the other one. There are many examples when ( c 2) is true but ( c 4) is not, and vise versa. For example, if outcome measures from both treatment groups are ordinal with the following distributions then μ Y − μ X = 0.1 > 0 but θ = −0.02 < 0. If we switch the distributions of X and Y , we will get μ Y − μ X < 0 but θ > 0. However, following theorem shows that (c2) and (c4) are equivalent when the distributions of endpoints in both groups are from Koopman-Darmois family.

Suppose observations from two treatments are independent to each other. Claims ( c 2) and ( c 4) are equivalent if data from both treatment groups have distributions from the Koopman-Darmois family, i.e., their probability distribution functions have the form p ( t ) = exp[ η i t − b ( η i ) − a ( t )] for treatment group i = 1, 2 respectively for some convex function b (·) and continuous function a (·).

Distributions in the Koopman-Darmois family include many outcome measures in practice. It includes: the normal distribution with common variance, chi-square distribution, exponential distribution, gamma distribution with common shape parameter, and Weibull distribution with common shape parameter. For discrete outcomes, it includes: the binomial distribution, geometric distribution, and Poisson distribution. Although ( c 2) and ( c 4) are equivalent in all these outcomes, claim ( c 4) imposes weaker assumptions than ( c 2) because the mean value for some distributions may not be finite or even not exist. Also, ( c 4) is invariant to any monotone transformation of the data while ( c 2) is not. This is because θ depends only on the relative rank order of the two random variables, not the actual values. For the example in Table 1 , we have μ Y − μ X = E [ Y − X ] = 0.1 > 0. However, if we use a square root transformation, we have E [ Y - X ] = - 0.01712 < 0 . Before and after the square root transformation, θ = −0.02. Note that, claim ( c 1) is never true in both situations. In fact, there is no stochastic ordering between the two treatment groups no matter how we transform the data. This shows that the sign of θ in claim ( c 4) does not tell whether claim ( c 1) is true. That is to say, ( c 1) may not be true even when ( c 4) is true.

Like claim ( c 4), claim ( c 3) is also invariant to any monotone transformation of the data. Following Theorem 3 shows that claim ( c 3) is also stronger than ( c 4), and they are the same if there are no ties in outcome measures between the two treatment groups.

If claim ( c 3) is true, then claim ( c 4) must be true. On the other hand, if the probability that the two treatments give the same outcome is zero, then both ( c 3) and ( c 4) are equivalent. In general, ( c 4) implies only med ( Y − X ) ≥ 0 but not med ( Y − X ) > 0 in ( c 3).

Based on Theorem 3, claims ( c 3) and ( c 4) are equivalent when the endpoint from both treatment groups follows continuous distributions. However, they are not equivalent when the endpoint is not continuous. As a simple counter example, let T = Y − X have mass function P ( T = −1) = 1/10, P ( T = 0) = 3/5, P ( T = 1) = 3/10, then its median m = 0. However, θ = P ( T = 1) − P ( T = −1) = 1/5 > 0. This means that ( c 4) is strictly weaker than ( c 3), i.e., ( c 3) is nested within ( c 4). Since both ( c 3) and ( c 5) are claims about medians, one may guess that claims ( c 3) and (c5) are the same. This is not true in general. For example, if P { X = 1} = P { Y = 2} = 0.6 and P { X = 3} = P { Y = 0} = 0.4, then (c5) is true but (c3) is not. If we switch X and Y ’s distributions, then ( c 3) is true but ( c 5) is not. However, the following theorem shows ( c 3) and ( c 5) are equivalent when outcomes from both treatment groups follow some symmetric distributions.

If outcomes from both treatment groups follow symmetric distributions, then

• claims ( c 2), ( c 3), and ( c 5) are equivalent,
• claim ( c 4) is at least as strong as claims ( c 2), ( c 3), and ( c 5),
• If ( c 2) is true and condition P {0 ≤ Y − X < 2( med Y − med x )} > 0 is satisfied, then claims ( c 3), ( c 4), and ( c 5) are all true.

Based on theorem 4(2), claim ( c 4) is generally stronger than claims ( c 3), ( c 3) and ( c 5) when the endpoints have symmetric distributions. This does not exclude the case when ( c 4) is equivalent to them. For example, when paired endpoints follow normal distributions, then (c3) and (c4) are equivalent based on Theorem 3 and Theorem 4(2).

5. HOW TO SELECT A CLAIM WHEN CONSTRUCTING HYPOTHESES?

When two claims are equivalent, we suggest choosing the test with higher statistical power. For example, when claim ( c 2) is equivalent to claim ( c 4), then we can choose a test that has higher power for either ( c 2) or ( c 4) depending on the shape of the distribution. We can choose a t -test when the shape is not too skewed and the Wilcoxon test when the shape is skewed.

6. CONCLUSION

We have discussed the problem of properly defining treatment superiority in clinical trials and the associated difficulties in statistical inference, namely, the need to precisely define the notion of superiority in a one-sided hypothesis test. We provided a theoretical framework for evaluating the statistical properties of different specifications of superiority in typical hypothesis testing. We proved the order of strength of five typical claims for alternative hypotheses. A summary of the relations among these five claims is given in Figure 2 . Some suggestions of how to select statistical parameters for treatment comparison were presented. This method can be applied to compare superiority for trials of different phases from early stage to comparative effectiveness. Because a two-sided hypothesis that two treatments are unequal can be viewed as a combination of two one-sided hypotheses, results presented in this paper would be useful for trials aimed to show inequality. Furthermore, it is worth noting that a Bayesian approach to compute the predictive probabilities P ( X < Y ) in both the parametric and the nonparametric model would be a natural alternative to what is presented. We plan to report findings using that approach in a future paper.

Relation among 5 claims

Acknowledgments

We want to thank the 4 reviewers for their insightful comments and suggestions that greatly improved the presentation of this paper. This work is partially supported by research grants 1R21NS043569-01, P50CA103175, MCRF-FHA05CRF, 1P01AG023630-049002, and P30CA006973.

Appendix I. Some Lemmas

Let F be any cumulative distribution function, Q F ( u ) is defined by ( 1 ). For any real constant t and 0 ≤ u ≤ 1, we have

Since CDF F must be a right continuous function, we have { x : F ( x ) ≥ u } is a closed convex set. Also, Q F ( u ) = inf{ x : F ( x ) ≥ u } is a limit point of this closed set, we have { x : F ( x ) ≥ u } = [ Q F ( u ), +∞).

To prove ( 2 ), suppose t < Q F ( u ) holds. Then t ∉ [ Q F ( u ), +∞) = { x : F ( x ) ≥ u }. This implies F ( t ) < u .

On the other hand, suppose F ( t ) < u holds. Then t ∉ { x : F ( x ) ≥ u } = [ Q F ( u ), +∞). This implies t < Q F ( u ).

If random variable X has cumulative distribution function F and quantile function defined by ( 1 ), U is a random variable with uniform distribution on closed interval [0, 1], then X 1 = Q F ( U ) has the same distribution as X , denoted as X 1 = D X .

X 1 has the same cumulative distribution function as X . Thus X 1 = D X .

If two cumulative distribution functions F and G satisfy F ( t 0 ) > G ( t 0 ) for some t 0 . Then there exists a closed interval ( t 2 , t 1 ) with t 2 < t 1 and a positive constant ε > 0 such that F ( t ) − G ( t ) ≥ ε for all t with t 2 < t < t 1 .

Let δ = [ F ( t 0 ) − G ( t 0 )]/4. Then δ > 0. Two open intervals A = ( G ( t 0 ) − δ , G ( t 0 ) + δ ) and B = ( F ( t 0 ) − δ , F ( t 0 ) + δ ) do not overlap with G ( t 0 ) + δ < F ( t 0 ) − δ . Since both F and G are right continuous and monotone non-decreasing, there exists sequences s 1 > s 2 > … > s n > … with G ( s n ) ∈ A , F ( s n ) ∈ B such that lim s n ↓ t 0 G ( s n ) = G ( t 0 ) < F ( t 0 ) = lim s n ↓ t 0 F ( s n ). As open intervals A and B do not overlap and both F and G are monotone non-decreasing, we have

Let ε = δ > 0, t 1 , t 2 be any two values in s 1 > s 2 > … > s n > …, say, t 1 = s 1 and t 2 = s 2 . Now, for any t with t 2 ≤ t ≤ t 1 , we have F ( t ) − G ( t ) ≥ F ( t 2 ) − G ( t 1 ) = F ( s 2 ) − G ( s 1 ) ≥ 3 δ > ε > 0 according to ( 3 ).

If Y ≻ X , i.e., P { Y > t } ≥ P { X > t } for all t , and there exists at least one t 0 such that P { Y > t 0 } > P { X > t 0 }. Then there exist random variables X 1 and Y 1 such that

• X 1 = D X and Y 1 = D Y ;
• X 1 ≤ Y 1 , and P { X 1 < Y 1 } > 0.

Let U be a random variable with uniform distribution on closed interval [0, 1], F ( t ) = P { X ≤ t }, G ( t ) = P { Y ≤ t }, X 1 = Q F ( U ), and Y 1 = Q G ( U ). Results in ( 1 ) can be obtained from Lemma 2.

According to Y ≻ X assumption, F ( t ) ≥ G ( t ) for all t . This implies that { t : F ( t ) ≥ U } ⊇ { t : G ( t ) ≥ U }. Hence X 1 = inf{ t : F ( t ) ≥ U } ≤ inf{ t : G ( t ) ≥ U } = Y 1 . Now it remains to show that P { X 1 < Y 1 } > 0. If this is not true, i.e., P { X 1 < Y 1 } = 0, then for any t ,

This contradicts F ( t 0 ) > G ( t 0 ) assumption. Thus, we must have P { X 1 < Y 1 } > 0.

Let G be the cumulative distribution function of some random variable Y . Then (i) P { G ( Y ) ≤ u } = u if u is in the range of G; P { G ( Y ) ≤ u } ≤ u if u ∈ (0, 1) is not in the range of G ; (ii) E [ G ( Y )] ≥ 1/2; (iii) Suppose t 1 , …, t n are discontinuous points of G . Then E [ G ( Y ) ] ≥ 1 + ∑ i = 1 n [ G ( t i ) - G ( t i - ) ] 2 .

Then p ( u ) ≥ u (Serfling, 1980, page 3), q ( u ) ≥ G −1 ( u ), and G ( q ( u )) ≥ G ( G −1 ( u )) ≥ u . Let’s prove (i) under the following three cases: (ia). p ( u ) > u ; (ib). p ( u ) = u and G ( q ( u )) > u ; (ic). p ( u ) = u and G ( q ( u )) = u .

• (ia) If G ( G −1 ( u )) = p ( u ) > u , the definition of G −1 ( u ) implies G ( G −1 ( u ) − ε ) < u for ∀ ε > 0; so { G ( Y ) = u } = ϕ (an empty set) and G ( G −1 ( u ) − 0) ≤ u . Now we have P { G ( Y ) ≤ u } = P { G ( Y ) < u } = P { Y < G −1 ( u )} (Serfling, 1980, page 3) = G ( G −1 ( u ) − 0) ≤ u .
• (ib) If p ( u ) = u and G ( q ( u )) > u . Definition of q ( u ) implies G ( q ( u ) − 0) ≤ u . Since 0 ≤ P { G −1 ( u ) < Y < q ( u )} = G ( q ( u ) − 0) − G ( G −1 ( u )) ≤ u − u = 0, i.e., G ( q ( u ) − 0) = G ( G −1 ( u )) = u , we have P { G ( Y ) ≤ u } = P { Y < q ( u )} = G ( q ( u ) − 0) = u .

In summary, we have showed that P { G ( Y ) ≤ u } ≤ u for ∀ u ∈ (0, 1). Suppose u is in the range of G : there exists y 0 such that G ( y 0 ) = u . Then P { G ( Y ) ≤ u } = P { G ( Y ) ≤ G ( y 0 )} = P { Y ≤ y 0 } = G ( y 0 ) = u . On the other hand, if u is not in the range of G , then there exists δ > 0 such that P { G ( Y ) ≤ u } = P { G ( Y ) ≤ u − δ } ≤ u − δ < u .

• (ii) From (i), E [ G ( Y ) ] = 1 - ∫ 0 1 P { G ( Y ) ≤ u } d u ≥ 1 - ∫ 0 1 udu = 1 / 2 .
• (iii) Denote a i = G ( t i −); b i = G ( t 0 ) > a; a n +1 = 1; b 0 = 0 ( i = 1, …, n ). Then P { G ( Y ) ≤ u } = a i for ∀ u ∈ ( a i , b i ], and P { G ( Y ) ≤ u } ≤ u for ∀ u ∈ ( b i , a i +1 ]. Now ∫ 0 1 P { G ( Y ) ≤ u } d u = ∑ i = 0 n ∫ b i a i + 1 P { G ( Y ) ≤ u } d u + ∑ i = 1 n ∫ a i b i P { G ( Y ) ≤ u } d u ≤ ∑ i = 0 n ∫ b i a i + 1 udu + ∑ i = 1 n ∫ a i b i a i d u = ∑ i = 0 n 1 2 ( a i + 1 2 - b i 2 ) + ∑ i = 1 n a i ( b i - a i ) = 1 2 - 1 2 ∑ i = 1 n ( b i - a i ) 2 ; E [ G ( Y ) ] = 1 - ∫ 0 1 P { G ( Y ) ≤ u } d u ≥ 1 2 + 1 2 ∑ i = 1 n ( b i - a i ) 2 .

Appendix II. Proof of Theorems

1. proof of theorem 1.

• Let F and G be the cumulative distribution functions of X and Y respectively. Then F ( t ) ≥ G ( t ) and G −1 [ F ( t )] ≥ t for all t since Y ≻ X . From Lemma 3, there exists some closed interval ( t 2 , t 1 ) with t 2 < t 1 and a positive constant ε > 0 such that F ( t ) − G ( t ) ≥ ε for all t with t 2 < t < t 1 . As both F and G are monotone right continuous distributions, this implies that G −1 [ F ( t )] > t for t 2 < t < t 1 . Now we have E [ X ] = ∫ tdF ( t ) < ∫ G −1 [ F ( t )] dF ( t ) = ∫ ydG ( y ) = E [ Y ] where transformation y = G −1 [ F ( t )] is used in the integral. This shows that claim (c1) is stronger than claim (c2).

Let F and G be the cumulative distribution functions of random variables X and Y respectively. By definition of Y ≻ X , there exists a finite point t 0 such that F ( t 0 ) > G ( t 0 ).

Define t 1 = inf{ t > t 0 : G ( t 0 ) < G ( t )}. Then t 0 ≤ t 1 < +∞, G ( t 1 − δ ) ≤ G ( t 0 ), and G ( t 1 + δ ) > G ( t 0 ) for ∀ δ > 0. We first show E [ F ( Y )] > 1/2. If G has jump at t 1 . Lemma 5 (iii) implies E [ F ( Y )] ≥ E [ G ( Y )] > 1/2.

If G is continuous at t 1 , then G ( t 0 ) ≤ G ( t 1 ) = lim n →∞ G ( t 1 − 1/ n ) ≤ G ( t 0 ), i.e., G ( t 1 ) = G ( t 0 ) < F ( t 0 ). Definition of t 1 and the right continuous of G imply that there exist constants δ 1 > 0 and ε 1 > 0 such that G ( t 1 + δ 1 ) < F ( t 0 ) − ε 1 . For any t ∈ ( t 0 , t 1 + δ 1 ]: G ( t ) ≤ G ( t 1 + δ 1 ) < F ( t 0 ) − ε 1 ≤ F ( t ) − ε 1 . Thus

Now we have θ = 2 P { Y ≥ X }−1 = 2 E [ F ( Y )]−1 > 0 (since P { X = Y } = 0. Thus claim (c1) is stronger than claim (c4).

To prove that claim (c1) is stronger than claim (c3), we refer to Theorem 3 where the equivalence between claim (c3) and (c4) under the assumption of P { X = Y } = 0 is proved.

• When data from both treatment groups have continuous distributions, we must have P { X = Y } = 0. From ( 1 ) and ( 2 ) above, we have showed that claim (c1) is stronger than claims (c2), (c3), and (c4). We now prove that claim (c1) is also stronger than claim (c5). Assumption P { Y > t } > P { X > t } for all t means that F ( t ) > G ( t ) for all t . Since F ( med X ) = 1/2 = G ( med Y ) by definition of median for continuous random variable, we have F ( med X ) = G ( med Y ) < F ( med Y ), i.e., F ( med X ) < F ( med Y ). Since F is a monotone increasing function, we must have med X < med Y , i.e., claim (c5) is true.

2. Proof of Theorem 2

Let X i be the random variable for treatment group i , i = 1, 2. Since b (·) is a convex function, E [ X 1 ] − E [ X 2 ] = ḃ ( η 1 ) − ḃ ( η 2 ) > 0 if and only if η 1 > η 2 . Then

This implies P ( X 1 > X 2 ) − P ( X 1 < X 2 ) > 0 if and only if η 1 > η 2 , completing the proof.

3. Proof of Theorem 3

Define m = med ( Y − X ) , T = Y − X and its distribution function H . Then

If (c3) is true, i.e., m > 0:

Thus, (c4) is true.

On the other hand, if P ( T = 0) = 0 and θ > 0, we use ( 4 ) to obtain H (0) = [1 − θ + P ( T = 0)]/2 < 1/2 ≤ H ( m ). Hence m > 0.

In general, if θ > 0, then, for any δ > 0, H (− δ ) ≤ H (0) − P ( T = 0) = 1/2 − θ /2 − P ( T = 0)/2 < 1/2, we have − δ ∉ { t: H ( t ) ≥ 1/2}. Hence m ≥ 0.

4. Proof of Theorem 4

Let m X and m Y be the medians of X and Y respectively. The symmetry assumption implies that

This means that Y − X is symmetric about m Y − m X . By symmetry, we have med Y − X = m Y − m X = med Y − med X . Thus (c3) and (c5) are equivalent. In summary, we have (c2), (c3), and (c5) are equivalent.

• Substituting t = − m Y + m X into ( 6 ) to obtain P { Y − X > 0} = P { Y − X < 2( m Y − m X )}. Thus θ = P { Y − X > 0} − P { Y − X < 0} = P { Y − X < 2( m Y − m X )} − P { Y − X > 0}. θ > 0 implies P { Y − X < 2( m Y − m X )} > P { Y − X > 0}. We must have 2( m Y − m X ) > 0, i.e., m Y > m X which is (c5). Based on claims in (1), we conclude that claim (c4) is at least as strong as claims (c2), (c3) and (c5).
• We only need to show that (c4) is true. From algebra in the proof of (2), θ = P { Y − X < 2( m Y − m X )} − P { Y − X > 0} = P {0 ≤ Y − X < 2( m Y − m X )} > 0. Thus θ > 0.

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https://educationhub.blog.gov.uk/2024/05/16/new-rshe-guidance-what-it-means-for-sex-education-lessons-in-schools/

New RSHE guidance: What it means for sex education lessons in schools

R elationships, Sex and Health Education (RSHE) is a subject taught at both primary and secondary school.  

In 2020, Relationships and Sex Education was made compulsory for all secondary school pupils in England and Health Education compulsory for all pupils in state-funded schools.  

Last year, the Prime Minister and Education Secretary brought forward the first review of the curriculum following reports of pupils being taught inappropriate content in RSHE in some schools.  

The review was informed by the advice of an independent panel of experts. The results of the review and updated guidance for consultation has now been published.   

We are now asking for views from parents, schools and others before the guidance is finalised. You can find the consultation here .   

What is new in the updated curriculum?  

Following the panel’s advice, w e’re introducing age limits, to ensure children aren’t being taught about sensitive and complex subjects before they are ready to fully understand them.    

We are also making clear that the concept of gender identity – the sense a person may have of their own gender, whether male, female or a number of other categories   – is highly contested and should not be taught. This is in line with the cautious approach taken in our gu idance on gender questioning children.  

Along with other factors, teaching this theory in the classroom could prompt some children to start to question their gender when they may not have done so otherwise, and is a complex theory for children to understand.   

The facts about biological sex and gender reassignment will still be taught.  

The guidance for schools also contains a new section on transparency with parents, making it absolutely clear that parents have a legal right to know what their children are being taught in RSHE and can request to see teaching materials.   

In addition, we’re seeking views on adding several new subjects to the curriculum, and more detail on others. These include:   

• Suicide prevention  
• Sexual harassment and sexual violence  
• L oneliness  
• The prevalence of 'deepfakes’  
• Healthy behaviours during pregnancy, as well as miscarriage  
• Illegal online behaviours including drug and knife supply  
• The dangers of vaping   
• Menstrual and gynaecological health including endometriosis, polycystic ovary syndrome (PCOS) and heavy menstrual bleeding.  

What are the age limits?   

In primary school, we’ve set out that subjects such as the risks about online gaming, social media and scams should not be taught before year 3.   

Puberty shouldn’t be taught before year 4, whilst sex education shouldn’t be taught before year 5, in line with what pupils learn about conception and birth as part of the national curriculum for science.  

In secondary school, issues regarding sexual harassment shouldn’t be taught before year 7, direct references to suicide before year 8 and any explicit discussion of sexual activity before year 9.  

Do schools have to follow the guidance?  

Following the consultation, the guidance will be statutory, which means schools must follow it unless there are exceptional circumstances.   

There is some flexibility w ithin the age ratings, as schools will sometimes need to respond to questions from pupils about age-restricted content, if they come up earlier within their school community.   

In these circumstances, schools are instructed to make sure that teaching is limited to the essential facts without going into unnecessary details, and parents should be informed.  

When will schools start teaching this?  

School s will be able to use the guidance as soon as we publish the final version later this year.   

However, schools will need time to make changes to their curriculum, so we will allow an implementation period before the guidance comes into force.     

What can parents do with these resources once they have been shared?

This guidance has openness with parents at its heart. Parents are not able to veto curriculum content, but they should be able to see what their children are being taught, which gives them the opportunity to raise issues or concerns through the school’s own processes, if they want to.

Parents can also share copyrighted materials they have received from their school more widely under certain circumstances.

If they are not able to understand materials without assistance, parents can share the materials with translators to help them understand the content, on the basis that the material is not shared further.

Copyrighted material can also be shared under the law for so-called ‘fair dealing’ - for the purposes of quotation, criticism or review, which could include sharing for the purpose of making a complaint about the material.

This could consist of sharing with friends, families, faith leaders, lawyers, school organisations, governing bodies and trustees, local authorities, Ofsted and the media.  In each case, the sharing of the material must be proportionate and accompanied by an acknowledgment of the author and its ownership.

Under the same principle, parents can also share relevant extracts of materials with the general public, but except in cases where the material is very small, it is unlikely that it would be lawful to share the entirety of the material.

These principles would apply to any material which is being made available for teaching in schools, even if that material was provided subject to confidentiality restrictions.

Do all children have to learn RSHE?  

Parents still have the right to withdraw their child from sex education, but not from the essential content covered in relationships educatio n.  

You may also be interested in:

• Education Secretary's letter to parents: You have the right to see RSHE lesson material
• Sex education: What is RSHE and can parents access curriculum materials?
• What do children and young people learn in relationship, sex and health education

Tags: age ratings , Gender , Relationships and Sex Education , RSHE , sex ed , Sex education

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• Published: 13 May 2024

Ethnography and ethnohistory support the efficiency of hunting through endurance running in humans

• Eugène Morin   ORCID: orcid.org/0000-0002-4840-0156 1 , 2 &
• Bruce Winterhalder   ORCID: orcid.org/0000-0001-6560-3302 3  

Nature Human Behaviour ( 2024 ) Cite this article

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• Archaeology
• Biological anthropology

Humans have two features rare in mammals: our locomotor muscles are dominated by fatigue-resistant fibres and we effectively dissipate through sweating the metabolic heat generated through prolonged, elevated activity. A promising evolutionary explanation of these features is the endurance pursuit (EP) hypothesis, which argues that both traits evolved to facilitate running down game by persistence. However, this hypothesis has faced two challenges: running is energetically costly and accounts of EPs among late twentieth century foragers are rare. While both observations appear to suggest that EPs would be ineffective, we use foraging theory to demonstrate that EPs can be quite efficient. We likewise analyse an ethnohistoric and ethnographic database of nearly 400 EP cases representing 272 globally distributed locations. We provide estimates for return rates of EPs and argue that these are comparable to other pre-modern hunting methods in specified contexts. EP hunting as a method of food procurement would have probably been available and attractive to Plio/Pleistocene hominins.

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Data availability.

All data presented in the paper are available in Supplementary Data 1 , along with citations to their sources.

Bramble, D. M. & Lieberman, D. E. Endurance running and the evolution of Homo . Nature 432 , 345–352 (2004).

Article   CAS   PubMed   Google Scholar  

Pontzer, H. Economy and endurance in human evolution. Curr. Biol. 27 , R613–R621 (2017).

Lieberman, D. E. Exercised: Why Something We Never Evolved to Do Is Healthy and Rewarding (Pantheon Books, 2020).

Carrier, D. R. The energetic paradox of human running and hominid evolution. Curr. Anthropol. 25 , 483–495 (1984).

Article   Google Scholar  

Bogdanis, G. C., Nevill, M., Lakomy, H. & Boobis, L. Power output and muscle metabolism during and following recovery from 10 and 20s of maximal sprint exercise in humans. Acta Physiol. Scand. 163 , 261–272 (1998).

Calbet, J. A. L. et al. Limitations to oxygen transport and utilization during sprint exercise in humans: evidence for a functional reserve in muscle O 2 diffusing capacity. J. Physiol. 593 , 4649–4664 (2015).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Sharp, N. C. C. Timed running speed of a cheetah ( Acinonyx jubatus ). J. Zool. 241 , 493–494 (1997).

O’Neill, M. C., Umberger, B. R., Holowka, N. B., Larson, S. G. & Reiser, P. J. Chimpanzee super strength and human skeletal muscle evolution. Proc. Natl Acad. Sci. USA 114 , 7343–7348 (2017).

Article   PubMed   PubMed Central   Google Scholar  

Queeno, S. R. et al. Human and African ape myosin heavy chain content and the evolution of hominin skeletal muscle. Comp. Biochem. Physiol. A https://doi.org/10.1016/j.cbpa.2023.111415 (2023).

Baker, J. S., McCormick, M. C. & Robergs, R. A. Interaction among skeletal muscle metabolic energy systems during intense exercise. J. Nutr. Metab. 2010 , 905612 (2010).

Mukund, K. & Subramaniam, S. Skeletal muscle: a review of molecular structure and function, in health and disease. Syst. Biol. Med. 12 , e1462 (2020).

Google Scholar  

Best, A. W. Why does strength training improve endurance performance? Am. J. Hum. Biol. 33 , e23526 (2020).

Article   PubMed   Google Scholar  

Schmidt-Nielsen, K. Desert Animals: Physiological Problems of Heat and Water (Clarendon Press, 1964).

Speakman, J. R. & Król, E. Maximal heat dissipation capacity and hyperthermia risk: neglected key factors in the ecology of endotherms. J. Anim. Ecol. 79 , 726–746 (2010).

Kamberov, Y. G. et al. Comparative evidence for the independent evolution of hair and sweat gland traits in primates. J. Hum. Evol. 125 , 99–105 (2018).

Saris, W. H. M. The role of sports foods in physical performance. in International Food Safety Handbook: Science, International Regulation, and Control (eds van der Heijden, K., Younes, M., Fishbein, L. & Miller, S.) Ch. 13 (CRC Press, 1999).

Newman, R. W. Why man is such a sweaty and thirsty naked animal: a speculative review. Hum. Biol. 42 , 12–27 (1970).

CAS   PubMed   Google Scholar  

Aldea, D. et al. Repeated mutation of a developmental enhancer contributed to human thermoregulatory evolution. Proc. Natl Acad. Sci. 118 , e2021722118 (2021).

Kamberov, Y. G. et al. A genetic basis of variation in eccrine sweat gland and hair follicle density. Proc. Natl Acad. Sci. USA 112 , 9932–9937 (2015).

Godfrey, R. W. et al. Evaluating the impact of breed, pregnancy, and hair coat on body temperature and sweating rate of hair sheep ewes in the tropics. J. Anim. Sci. 95 , 2936–2942 (2017).

Asres, A. & Amha, N. Physiological adaptation of animals to the change of environment: a review. J. Biol. Agric. Healthc. 4 , 146–151 (2014).

Hora, M., Pontzer, H., Struška, M., Entin, P. & Sládek, V. Comparing walking and running in persistence hunting. J. Hum. Evol. 172 , 103247 (2022).

Montagu, A. Natural selection and man’s relative hairlessness. JAMA 187 , 356–357 (1964).

Krantz, G. S. Brain size and hunting ability in earliest man. Curr. Anthropol. 9 , 450–451 (1968).

Watanabe, H. Running, creeping and climbing: a new ecological and evolutionary perspective on human locomotion. Mankind 8 , 1–13 (1971).

Bortz, W. M. Physical exercise as an evolutionary force. J. Hum. Evol. 14 , 145–155 (1985).

MacArthur, R. H. & Pianka, E. R. On optimal use of a patchy environment. Am. Nat. 100 , 603–609 (1966).

Bramble, D. M. & Carrier, D. R. Running and breathing in mammals. Science 219 , 251–256 (1983).

Lieberman, D. E., Bramble, D. M., Raichlen, D. A. & Shea, J. J. in The First Humans - Origin and Early Evolution of the Genus Homo (eds Grine, F. E. et al.) 77–92 (Springer, 2009).

Liebenberg, L. Persistence hunting by modern hunter‐gatherers. Curr. Anthropol. 47 , 1017–1026 (2006).

Liebenberg, L. The relevance of persistence hunting to human evolution. J. Hum. Evol. 55 , 1156–1159 (2008).

Hora, M., Pontzer, H., Wall-Scheffler, C. M. & Sládek, V. Dehydration and persistence hunting in Homo erectus . J. Hum. Evol. 138 , 102682 (2020).

Ruxton, G. D. & Wilkinson, D. M. Endurance running and its relevance to scavenging by early hominins. Evolution 67 , 861–867 (2013).

Glaub, M. & Hall, C. A. S. Evolutionary implications of persistence hunting: an examination of energy return on investment for !Kung hunting. Hum. Ecol. 45 , 393–401 (2017).

Vidal-Cordasco, M., Rodriguez, J., Prado-Novoa, O., Zorrilla-Revilla, G. & Mateos, A. Locomotor economy and foraging ecology in hominins. J. Anthropol. Res. 77 , 338–361 (2021).

Lieberman, D. E., Bramble, D. M., Raichlen, D. A. & Shea, J. J. The evolution of endurance running and the tyranny of ethnography: a reply to Pickering and Bunn (2007). J. Hum. Evol. 53 , 439–442 (2007).

Marino, F. E., Sibson, B. E. & Lieberman, D. E. The evolution of human fatigue resistance. J. Comp. Physiol. B 192 , 411–422 (2022).

Pickering, T. R. & Bunn, H. T. The endurance running hypothesis and hunting and scavenging in savanna-woodlands. J. Hum. Evol. 53 , 434–438 (2007).

Rathkey, J. K. & Wall‐Scheffler, C. M. People choose to run at their optimal speed. Am. J. Phys. Anthropol. 163 , 85–93 (2017).

Steudel-Numbers, K. L. & Wall-Scheffler, C. M. Optimal running speed and the evolution of hominin hunting strategies. J. Hum. Evol. 56 , 355–360 (2009).

Lieberman, D. E. et al. Running in Tarahumara (Rarámuri) culture: persistence hunting, footracing, dancing, work, and the fallacy of the athletic savage. Curr. Anthropol. 61 , 356–379 (2020).

Morin, E., Bird, D., Winterhalder, B. & Bliege Bird, R. Deconstructing hunting returns: can we reconstruct and predict payoffs from pursuing prey? J. Archaeol. Method Theory 29 , 561–623 (2022).

Schoener, T. W. The compression hypothesis and temporal resource partitioning. Proc. Natl Acad. Sci. USA 71 , 4169–4172 (1974).

Ember, C. R. Cross-Cultural Research Methods (AltaMira Press, 2009).

Watts, J. et al. Building quantitative cross-cultural databases from ethnographic records: promise, problems and principles. Cross Cult. Res. 56 , 62–94 (2022).

Jackson, J. C. et al. Supernatural explanations across 114 societies are more common for natural than social phenomena. Nat. Hum. Behav. 7 , 707–717 (2023).

Baughman, M. In pursuit of an ancient pursuit. Sports Illustrated 48 , 45–46 (1978).

Parker, K. L. & Robbins, C. T. Thermoregulation in mule deer and elk. Can. J. Zool. 62 , 1409–1422 (1984).

Morin, E., Bird, D., Winterhalder, B. & Bliege Bird, R. Why do humans hunt cooperatively? Ethnohistoric data reveal the contexts, advantages and evolutionary importance of communal hunting. Curr. Anthropol. (in the press).

Bunn, H. T. & Pickering, T. R. Bovid mortality profiles in paleoecological context falsify hypotheses of endurance running–hunting and passive scavenging by early Pleistocene hominins. Quat. Res. 74 , 395–404 (2010).

Ross, C. T. & Winterhalder, B. Sit-and-wait versus active-search hunting: a behavioral ecological model of optimal search mode. J. Theor. Biol. 387 , 76–87 (2015).

Hitchcock, R. K. et al. The ethnoarchaeology of ambush hunting: a case study of ǂGi pan, Western Ngamiland, Botswana. Afr. Archaeol. Rev. 36 , 119–144 (2019).

Cher, P. H., Stewart, I. B. & Worringham, C. J. Minimum cost of transport in human running is not ubiquitous. Med. Sci. Sports Exerc. 47 , 307–314 (2015).

Selinger, J. C. et al. Running in the wild: energetics explain ecological running speeds. Curr. Biol. 32 , 2309–2315.e3 (2022).

Hassmén, P. Perceptual and physiological responses to cycling and running in groups of trained and untrained subjects. Eur. J. Appl. Physiol. Occup. Physiol. 60 , 445–451 (1990).

O’Connell, J. F., Hawkes, K. & Jones, N. B. Hadza scavenging: implications for Plio/Pleistocene hominid subsistence. Curr. Anthropol. 29 , 356–363 (1988).

Boesch, C. Cooperative hunting roles among Tai chimpanzees. Hum. Nat. 13 , 27–46 (2002).

Samuni, L., Wegdell, F. & Surbeck, M. Behavioural diversity of bonobo prey preference as a potential cultural trait. eLife 9 , e59191 (2020).

Durnin, J. V. G. A. & Passmore, R. Energy, Work and Leisure (Heinemann Educational Books, 1967).

McArdle, W. D., Katch, F. I. & Katch, V. L. Exercise Physiology: Nutrition, Energy, and Human Performance 7th edn (Wolters Kluwer, 2010).

Mason, M. H. The Arctic Forests (Hodder and Stoughton, 1924).

Howley, J. P. The Beothucks or Red Indians: The Aboriginal Inhabitants of Newfoundland (Cambridge Univ. Press, 1915).

Cabeza de Vaca, A. N. The Journey of Alvar Nuñez Cabeza de Vaca (The Rio Grande Press, 1964).

Kroeber, A. L. Handbook of the Indians of California Bulletin No. 78 (Bureau of American Ethnology, 1925).

Cook, W. A. Through the Wildernesses of Brazil by Horse, Canoe and Float (American Tract Society, 1909).

Kronenberg, A. & Schütze, F. Teda of Tibesti. in Wiener Beiträge zur Kulturgeschichte und Linguistik . HRAF ms: 1, 6, 144, 28 l. [Original: 14, 160, 17 end plates] (Verlag Ferdinand Berger, 1958).

St. John, S. Life in the Forests of the Far East; or Travels in Northern Borneo Vol. 2, 2nd edn, Revised (Smith, Elder and Co., 1863).

Dutton, C. E. Hawaiian Volcanoes (Government Printing Office, 1883).

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Acknowledgements

Financial support for this research was provided by a Trent internal SSHRC grant (no. 53-51637). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. This study greatly benefited from discussions with and/or comments from the following people: A. Best, D. Bird, R. B. Bird, D. Bramble, D. Carrier, S. Gerety, M. Grote, M. Hora, J. C. Jackson, J. Koster, D. Lieberman, J. F. O’Connell, J. Speth, F. M. and A. Stein, M. Vidal-Cordasco and C. Wall-Scheffler. C. Wall-Scheffler generously provided the regression formula used in Fig. 1 and Supplementary Information .

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Eugène Morin

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Morin, E., Winterhalder, B. Ethnography and ethnohistory support the efficiency of hunting through endurance running in humans. Nat Hum Behav (2024). https://doi.org/10.1038/s41562-024-01876-x

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Received : 24 August 2023

Accepted : 28 March 2024

Published : 13 May 2024

DOI : https://doi.org/10.1038/s41562-024-01876-x

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