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• How to Write a Strong Hypothesis | Guide & Examples

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.

Table of contents

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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How to Develop a Good Research Hypothesis

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

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

Table of Contents

What is Hypothesis?

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

What is a Research Hypothesis?

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

Characteristics of a Good Research Hypothesis

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

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

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

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

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

Source: Educational Hub

How to formulate a research hypothesis.

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

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

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

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

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

3. Define the variables

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

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

4. Scrutinize the hypothesis

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

Types of Research Hypothesis

The types of research hypothesis are stated below:

1. Simple Hypothesis

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

2. Complex Hypothesis

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

3. Directional Hypothesis

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

4. Non-directional Hypothesis

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

5. Associative and Causal Hypothesis

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

6. Null Hypothesis

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

7. Alternative Hypothesis

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

Research Hypothesis Examples of Independent and Dependent Variables

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

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

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

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

Importance of a Testable Hypothesis

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

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

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

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

Frequently Asked Questions

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

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

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

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

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

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

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

Useful piece!

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

Nicely explained

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

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clear and concise. thanks.

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How to Write a Hypothesis: A Step-by-Step Guide

Introduction

An overview of the research hypothesis, different types of hypotheses, variables in a hypothesis, how to formulate an effective research hypothesis, designing a study around your hypothesis.

The scientific method can derive and test predictions as hypotheses. Empirical research can then provide support (or lack thereof) for the hypotheses. Even failure to find support for a hypothesis still represents a valuable contribution to scientific knowledge. Let's look more closely at the idea of the hypothesis and the role it plays in research.

As much as the term exists in everyday language, there is a detailed development that informs the word "hypothesis" when applied to research. A good research hypothesis is informed by prior research and guides research design and data analysis , so it is important to understand how a hypothesis is defined and understood by researchers.

What is the simple definition of a hypothesis?

A hypothesis is a testable prediction about an outcome between two or more variables . It functions as a navigational tool in the research process, directing what you aim to predict and how.

What is the hypothesis for in research?

In research, a hypothesis serves as the cornerstone for your empirical study. It not only lays out what you aim to investigate but also provides a structured approach for your data collection and analysis.

Essentially, it bridges the gap between the theoretical and the empirical, guiding your investigation throughout its course.

What is an example of a hypothesis?

If you are studying the relationship between physical exercise and mental health, a suitable hypothesis could be: "Regular physical exercise leads to improved mental well-being among adults."

This statement constitutes a specific and testable hypothesis that directly relates to the variables you are investigating.

What makes a good hypothesis?

A good hypothesis possesses several key characteristics. Firstly, it must be testable, allowing you to analyze data through empirical means, such as observation or experimentation, to assess if there is significant support for the hypothesis. Secondly, a hypothesis should be specific and unambiguous, giving a clear understanding of the expected relationship between variables. Lastly, it should be grounded in existing research or theoretical frameworks , ensuring its relevance and applicability.

Understanding the types of hypotheses can greatly enhance how you construct and work with hypotheses. While all hypotheses serve the essential function of guiding your study, there are varying purposes among the types of hypotheses. In addition, all hypotheses stand in contrast to the null hypothesis, or the assumption that there is no significant relationship between the variables .

Here, we explore various kinds of hypotheses to provide you with the tools needed to craft effective hypotheses for your specific research needs. Bear in mind that many of these hypothesis types may overlap with one another, and the specific type that is typically used will likely depend on the area of research and methodology you are following.

Null hypothesis

The null hypothesis is a statement that there is no effect or relationship between the variables being studied. In statistical terms, it serves as the default assumption that any observed differences are due to random chance.

For example, if you're studying the effect of a drug on blood pressure, the null hypothesis might state that the drug has no effect.

Alternative hypothesis

Contrary to the null hypothesis, the alternative hypothesis suggests that there is a significant relationship or effect between variables.

Using the drug example, the alternative hypothesis would posit that the drug does indeed affect blood pressure. This is what researchers aim to prove.

Simple hypothesis

A simple hypothesis makes a prediction about the relationship between two variables, and only two variables.

For example, "Increased study time results in better exam scores." Here, "study time" and "exam scores" are the only variables involved.

Complex hypothesis

A complex hypothesis, as the name suggests, involves more than two variables. For instance, "Increased study time and access to resources result in better exam scores." Here, "study time," "access to resources," and "exam scores" are all variables.

This hypothesis refers to multiple potential mediating variables. Other hypotheses could also include predictions about variables that moderate the relationship between the independent variable and dependent variable .

Directional hypothesis

A directional hypothesis specifies the direction of the expected relationship between variables. For example, "Eating more fruits and vegetables leads to a decrease in heart disease."

Here, the direction of heart disease is explicitly predicted to decrease, due to effects from eating more fruits and vegetables. All hypotheses typically specify the expected direction of the relationship between the independent and dependent variable, such that researchers can test if this prediction holds in their data analysis .

Statistical hypothesis

A statistical hypothesis is one that is testable through statistical methods, providing a numerical value that can be analyzed. This is commonly seen in quantitative research .

For example, "There is a statistically significant difference in test scores between students who study for one hour and those who study for two."

Empirical hypothesis

An empirical hypothesis is derived from observations and is tested through empirical methods, often through experimentation or survey data . Empirical hypotheses may also be assessed with statistical analyses.

For example, "Regular exercise is correlated with a lower incidence of depression," could be tested through surveys that measure exercise frequency and depression levels.

Causal hypothesis

A causal hypothesis proposes that one variable causes a change in another. This type of hypothesis is often tested through controlled experiments.

For example, "Smoking causes lung cancer," assumes a direct causal relationship.

Associative hypothesis

Unlike causal hypotheses, associative hypotheses suggest a relationship between variables but do not imply causation.

For instance, "People who smoke are more likely to get lung cancer," notes an association but doesn't claim that smoking causes lung cancer directly.

Relational hypothesis

A relational hypothesis explores the relationship between two or more variables but doesn't specify the nature of the relationship.

For example, "There is a relationship between diet and heart health," leaves the nature of the relationship (causal, associative, etc.) open to interpretation.

Logical hypothesis

A logical hypothesis is based on sound reasoning and logical principles. It's often used in theoretical research to explore abstract concepts, rather than being based on empirical data.

For example, "If all men are mortal and Socrates is a man, then Socrates is mortal," employs logical reasoning to make its point.

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In any research hypothesis, variables play a critical role. These are the elements or factors that the researcher manipulates, controls, or measures. Understanding variables is essential for crafting a clear, testable hypothesis and for the stages of research that follow, such as data collection and analysis.

In the realm of hypotheses, there are generally two types of variables to consider: independent and dependent. Independent variables are what you, as the researcher, manipulate or change in your study. It's considered the cause in the relationship you're investigating. For instance, in a study examining the impact of sleep duration on academic performance, the independent variable would be the amount of sleep participants get.

Conversely, the dependent variable is the outcome you measure to gauge the effect of your manipulation. It's the effect in the cause-and-effect relationship. The dependent variable thus refers to the main outcome of interest in your study. In the same sleep study example, the academic performance, perhaps measured by exam scores or GPA, would be the dependent variable.

Beyond these two primary types, you might also encounter control variables. These are variables that could potentially influence the outcome and are therefore kept constant to isolate the relationship between the independent and dependent variables . For example, in the sleep and academic performance study, control variables could include age, diet, or even the subject of study.

By clearly identifying and understanding the roles of these variables in your hypothesis, you set the stage for a methodologically sound research project. It helps you develop focused research questions, design appropriate experiments or observations, and carry out meaningful data analysis . It's a step that lays the groundwork for the success of your entire study.

Crafting a strong, testable hypothesis is crucial for the success of any research project. It sets the stage for everything from your study design to data collection and analysis . Below are some key considerations to keep in mind when formulating your hypothesis:

• Be specific : A vague hypothesis can lead to ambiguous results and interpretations . Clearly define your variables and the expected relationship between them.
• Ensure testability : A good hypothesis should be testable through empirical means, whether by observation , experimentation, or other forms of data analysis.
• Ground in literature : Before creating your hypothesis, consult existing research and theories. This not only helps you identify gaps in current knowledge but also gives you valuable context and credibility for crafting your hypothesis.
• Use simple language : While your hypothesis should be conceptually sound, it doesn't have to be complicated. Aim for clarity and simplicity in your wording.
• State direction, if applicable : If your hypothesis involves a directional outcome (e.g., "increase" or "decrease"), make sure to specify this. You also need to think about how you will measure whether or not the outcome moved in the direction you predicted.
• Keep it focused : One of the common pitfalls in hypothesis formulation is trying to answer too many questions at once. Keep your hypothesis focused on a specific issue or relationship.
• Account for control variables : Identify any variables that could potentially impact the outcome and consider how you will control for them in your study.
• Be ethical : Make sure your hypothesis and the methods for testing it comply with ethical standards , particularly if your research involves human or animal subjects.

Designing your study involves multiple key phases that help ensure the rigor and validity of your research. Here we discuss these crucial components in more detail.

Literature review

Starting with a comprehensive literature review is essential. This step allows you to understand the existing body of knowledge related to your hypothesis and helps you identify gaps that your research could fill. Your research should aim to contribute some novel understanding to existing literature, and your hypotheses can reflect this. A literature review also provides valuable insights into how similar research projects were executed, thereby helping you fine-tune your own approach.

Research methods

Choosing the right research methods is critical. Whether it's a survey, an experiment, or observational study, the methodology should be the most appropriate for testing your hypothesis. Your choice of methods will also depend on whether your research is quantitative, qualitative, or mixed-methods. Make sure the chosen methods align well with the variables you are studying and the type of data you need.

Preliminary research

Before diving into a full-scale study, it’s often beneficial to conduct preliminary research or a pilot study . This allows you to test your research methods on a smaller scale, refine your tools, and identify any potential issues. For instance, a pilot survey can help you determine if your questions are clear and if the survey effectively captures the data you need. This step can save you both time and resources in the long run.

Data analysis

Finally, planning your data analysis in advance is crucial for a successful study. Decide which statistical or analytical tools are most suited for your data type and research questions . For quantitative research, you might opt for t-tests, ANOVA, or regression analyses. For qualitative research , thematic analysis or grounded theory may be more appropriate. This phase is integral for interpreting your results and drawing meaningful conclusions in relation to your research question.

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• Manuscript Preparation

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

• 4 minute read

Table of Contents

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

What is a Hypothesis in Research?

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

Research Question vs Hypothesis

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

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

How to Write Hypothesis in Research

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

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

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

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

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

Research Hypothesis Example

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

Here are a few generic examples to get you started.

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

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

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

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

Language Editing Plus

Elsevier’s Language Editing Plus service can help ensure that your research hypothesis is well-designed, and articulates your research and conclusions. Our most comprehensive editing package, you can count on a thorough language review by native-English speakers who are PhDs or PhD candidates. We’ll check for effective logic and flow of your manuscript, as well as document formatting for your chosen journal, reference checks, and much more.

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How to Write a Research Hypothesis

• Research Process
• Peer Review

Since grade school, we've all been familiar with hypotheses. The hypothesis is an essential step of the scientific method. But what makes an effective research hypothesis, how do you create one, and what types of hypotheses are there? We answer these questions and more.

Updated on April 27, 2022

What is a research hypothesis?

General hypothesis.

Since grade school, we've all been familiar with the term “hypothesis.” A hypothesis is a fact-based guess or prediction that has not been proven. It is an essential step of the scientific method. The hypothesis of a study is a drive for experimentation to either prove the hypothesis or dispute it.

Research Hypothesis

A research hypothesis is more specific than a general hypothesis. It is an educated, expected prediction of the outcome of a study that is testable.

What makes an effective research hypothesis?

A good research hypothesis is a clear statement of the relationship between a dependent variable(s) and independent variable(s) relevant to the study that can be disproven.

Research hypothesis checklist

Once you've written a possible hypothesis, make sure it checks the following boxes:

• It must be testable: You need a means to prove your hypothesis. If you can't test it, it's not a hypothesis.
• It must include a dependent and independent variable: At least one independent variable ( cause ) and one dependent variable ( effect ) must be included.
• The language must be easy to understand: Be as clear and concise as possible. Nothing should be left to interpretation.
• It must be relevant to your research topic: You probably shouldn't be talking about cats and dogs if your research topic is outer space. Stay relevant to your topic.

How to create an effective research hypothesis

Pose it as a question first.

Start your research hypothesis from a journalistic approach. Ask one of the five W's: Who, what, when, where, or why.

A possible initial question could be: Why is the sky blue?

Do the preliminary research

Once you have a question in mind, read research around your topic. Collect research from academic journals.

If you're looking for information about the sky and why it is blue, research information about the atmosphere, weather, space, the sun, etc.

Write a draft hypothesis

Once you're comfortable with your subject and have preliminary knowledge, create a working hypothesis. Don't stress much over this. Your first hypothesis is not permanent. Look at it as a draft.

Your first draft of a hypothesis could be: Certain molecules in the Earth's atmosphere are responsive to the sky being the color blue.

Make your working draft perfect

Take your working hypothesis and make it perfect. Narrow it down to include only the information listed in the “Research hypothesis checklist” above.

Now that you've written your working hypothesis, narrow it down. Your new hypothesis could be: Light from the sun hitting oxygen molecules in the sky makes the color of the sky appear blue.

Write a null hypothesis

Your null hypothesis should be the opposite of your research hypothesis. It should be able to be disproven by your research.

In this example, your null hypothesis would be: Light from the sun hitting oxygen molecules in the sky does not make the color of the sky appear blue.

Why is it important to have a clear, testable hypothesis?

One of the main reasons a manuscript can be rejected from a journal is because of a weak hypothesis. “Poor hypothesis, study design, methodology, and improper use of statistics are other reasons for rejection of a manuscript,” says Dr. Ish Kumar Dhammi and Dr. Rehan-Ul-Haq in Indian Journal of Orthopaedics.

According to Dr. James M. Provenzale in American Journal of Roentgenology , “The clear declaration of a research question (or hypothesis) in the Introduction is critical for reviewers to understand the intent of the research study. It is best to clearly state the study goal in plain language (for example, “We set out to determine whether condition x produces condition y.”) An insufficient problem statement is one of the more common reasons for manuscript rejection.”

Characteristics that make a hypothesis weak include:

• Unclear variables
• Unoriginality
• Too general
• Too specific

A weak hypothesis leads to weak research and methods . The goal of a paper is to prove or disprove a hypothesis - or to prove or disprove a null hypothesis. If the hypothesis is not a dependent variable of what is being studied, the paper's methods should come into question.

A strong hypothesis is essential to the scientific method. A hypothesis states an assumed relationship between at least two variables and the experiment then proves or disproves that relationship with statistical significance. Without a proven and reproducible relationship, the paper feeds into the reproducibility crisis. Learn more about writing for reproducibility .

In a study published in The Journal of Obstetrics and Gynecology of India by Dr. Suvarna Satish Khadilkar, she reviewed 400 rejected manuscripts to see why they were rejected. Her studies revealed that poor methodology was a top reason for the submission having a final disposition of rejection.

Aside from publication chances, Dr. Gareth Dyke believes a clear hypothesis helps efficiency.

“Developing a clear and testable hypothesis for your research project means that you will not waste time, energy, and money with your work,” said Dyke. “Refining a hypothesis that is both meaningful, interesting, attainable, and testable is the goal of all effective research.”

Types of research hypotheses

There can be overlap in these types of hypotheses.

Simple hypothesis

A simple hypothesis is a hypothesis at its most basic form. It shows the relationship of one independent and one independent variable.

Example: Drinking soda (independent variable) every day leads to obesity (dependent variable).

Complex hypothesis

A complex hypothesis shows the relationship of two or more independent and dependent variables.

Example: Drinking soda (independent variable) every day leads to obesity (dependent variable) and heart disease (dependent variable).

Directional hypothesis

A directional hypothesis guesses which way the results of an experiment will go. It uses words like increase, decrease, higher, lower, positive, negative, more, or less. It is also frequently used in statistics.

Example: Humans exposed to radiation have a higher risk of cancer than humans not exposed to radiation.

Non-directional hypothesis

A non-directional hypothesis says there will be an effect on the dependent variable, but it does not say which direction.

Associative hypothesis

An associative hypothesis says that when one variable changes, so does the other variable.

Alternative hypothesis

An alternative hypothesis states that the variables have a relationship.

• The opposite of a null hypothesis

Example: An apple a day keeps the doctor away.

Null hypothesis

A null hypothesis states that there is no relationship between the two variables. It is posed as the opposite of what the alternative hypothesis states.

Researchers use a null hypothesis to work to be able to reject it. A null hypothesis:

• Can never be proven
• Can only be rejected
• Is the opposite of an alternative hypothesis

Example: An apple a day does not keep the doctor away.

Logical hypothesis

A logical hypothesis is a suggested explanation while using limited evidence.

Example: Bats can navigate in the dark better than tigers.

In this hypothesis, the researcher knows that tigers cannot see in the dark, and bats mostly live in darkness.

Empirical hypothesis

An empirical hypothesis is also called a “working hypothesis.” It uses the trial and error method and changes around the independent variables.

• An apple a day keeps the doctor away.
• Two apples a day keep the doctor away.
• Three apples a day keep the doctor away.

In this case, the research changes the hypothesis as the researcher learns more about his/her research.

Statistical hypothesis

A statistical hypothesis is a look of a part of a population or statistical model. This type of hypothesis is especially useful if you are making a statement about a large population. Instead of having to test the entire population of Illinois, you could just use a smaller sample of people who live there.

Example: 70% of people who live in Illinois are iron deficient.

Causal hypothesis

A causal hypothesis states that the independent variable will have an effect on the dependent variable.

Example: Using tobacco products causes cancer.

Final thoughts

Make sure your research is error-free before you send it to your preferred journal . Check our our English Editing services to avoid your chances of desk rejection.

Jonny Rhein, BA

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

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

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when  conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a  correlational study  can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses .  R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

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

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

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

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

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

Research Hypothesis 101

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

What is a hypothesis?

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

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

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

Hypothesis: sleep impacts academic performance.

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

But that’s not good enough…

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

What is a research hypothesis?

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

Let’s take a look at these more closely.

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

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

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

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

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

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

Hypothesis Essential #2: Testability (Provability)

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

For example, consider the hypothesis we mentioned earlier:

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

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

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

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

Defining A Research Hypothesis

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

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

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

What about the null hypothesis?

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

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

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

And there you have it – hypotheses in a nutshell. 

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

Psst... there’s more!

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

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16 Comments

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

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

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

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

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

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

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

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

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

could you please elaborate it more

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

This is very helpful

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

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

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

this is very important note help me much more

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How to Write a Hypothesis – Steps & Tips

Published by Alaxendra Bets at August 14th, 2021 , Revised On October 26, 2023

What is a Research Hypothesis?

You can test a research statement with the help of experimental or theoretical research, known as a hypothesis.

If you want to find out the similarities, differences, and relationships between variables, you must write a testable hypothesis before compiling the data, performing analysis, and generating results to complete.

The data analysis and findings will help you test the hypothesis and see whether it is true or false. Here is all you need to know about how to write a hypothesis for a  dissertation .

Research Hypothesis Definition

Not sure what the meaning of the research hypothesis is?

A research hypothesis predicts an answer to the research question  based on existing theoretical knowledge or experimental data.

Some studies may have multiple hypothesis statements depending on the research question(s).  A research hypothesis must be based on formulas, facts, and theories. It should be testable by data analysis, observations, experiments, or other scientific methodologies that can refute or support the statement.

Variables in Hypothesis

Developing a hypothesis is easy. Most research studies have two or more variables in the hypothesis, particularly studies involving correlational and experimental research. The researcher can control or change the independent variable(s) while measuring and observing the independent variable(s).

“How long a student sleeps affects test scores.”

In the above statement, the dependent variable is the test score, while the independent variable is the length of time spent in sleep. Developing a hypothesis will be easy if you know your research’s dependent and independent variables.

Once you have developed a thesis statement, questions such as how to write a hypothesis for the dissertation and how to test a research hypothesis become pretty straightforward.

Looking for dissertation help?

Researchprospect to the rescue then.

We have expert writers on our team who are skilled at helping students with quantitative dissertations across a variety of STEM disciplines. Guaranteeing 100% satisfaction!

Step-by-Step Guide on How to Write a Hypothesis

Here are the steps involved in how to write a hypothesis for a dissertation.

Step 1: Start with a Research Question

• Begin by asking a specific question about a topic of interest.
• This question should be clear, concise, and researchable.

Example: Does exposure to sunlight affect plant growth?

Step 2: Do Preliminary Research

• Before formulating a hypothesis, conduct background research to understand existing knowledge on the topic.
• Familiarise yourself with prior studies, theories, or observations related to the research question.

Step 3: Define Variables

• Independent Variable (IV): The factor that you change or manipulate in an experiment.
• Dependent Variable (DV): The factor that you measure.

Example: IV: Amount of sunlight exposure (e.g., 2 hours/day, 4 hours/day, 8 hours/day) DV: Plant growth (e.g., height in centimetres)

Step 4: Formulate the Hypothesis

• A hypothesis is a statement that predicts the relationship between variables.
• It is often written as an “if-then” statement.

Example: If plants receive more sunlight, then they will grow taller.

Step 5: Ensure it is Testable

A good hypothesis is empirically testable. This means you should be able to design an experiment or observation to test its validity.

Example: You can set up an experiment where plants are exposed to varying amounts of sunlight and then measure their growth over a period of time.

Step 6: Consider Potential Confounding Variables

• Confounding variables are factors other than the independent variable that might affect the outcome.
• It is important to identify these to ensure that they do not skew your results.

Example: Soil quality, water frequency, or type of plant can all affect growth. Consider keeping these constant in your experiment.

Step 7: Write the Null Hypothesis

• The null hypothesis is a statement that there is no effect or no relationship between the variables.
• It is what you aim to disprove or reject through your research.

Example: There is no difference in plant growth regardless of the amount of sunlight exposure.

Step 8: Test your Hypothesis

Design an experiment or conduct observations to test your hypothesis.

Example: Grow three sets of plants: one set exposed to 2 hours of sunlight daily, another exposed to 4 hours, and a third exposed to 8 hours. Measure and compare their growth after a set period.

Step 9: Analyse the Results

After testing, review your data to determine if it supports your hypothesis.

Step 10: Draw Conclusions

• Based on your findings, determine whether you can accept or reject the hypothesis.
• Remember, even if you reject your hypothesis, it’s a valuable result. It can guide future research and refine questions.

Three Ways to Phrase a Hypothesis

Try to use “if”… and “then”… to identify the variables. The independent variable should be present in the first part of the hypothesis, while the dependent variable will form the second part of the statement. Consider understanding the below research hypothesis example to create a specific, clear, and concise research hypothesis;

If an obese lady starts attending Zomba fitness classes, her health will improve.

In academic research, you can write the predicted variable relationship directly because most research studies correlate terms.

The number of Zomba fitness classes attended by the obese lady has a positive effect on health.

If your research compares two groups, then you can develop a hypothesis statement on their differences.

An obese lady who attended most Zumba fitness classes will have better health than those who attended a few.

How to Write a Null Hypothesis

If a statistical analysis is involved in your research, then you must create a null hypothesis. If you find any relationship between the variables, then the null hypothesis will be the default position that there is no relationship between them. H0 is the symbol for the null hypothesis, while the hypothesis is represented as H1. The null hypothesis will also answer your question, “How to test the research hypothesis in the dissertation.”

H0: The number of Zumba fitness classes attended by the obese lady does not affect her health.

H1: The number of Zumba fitness classes attended by obese lady positively affects health.

Also see:  Your Dissertation in Education

Hypothesis Examples

Research Question: Does the amount of sunlight a plant receives affect its growth? Hypothesis: Plants that receive more sunlight will grow taller than plants that receive less sunlight.

Research Question: Do students who eat breakfast perform better in school exams than those who don’t? Hypothesis: Students who eat a morning breakfast will score higher on school exams compared to students who skip breakfast.

Research Question: Does listening to music while studying impact a student’s ability to retain information? Hypothesis 1 (Directional): Students who listen to music while studying will retain less information than those who study in silence. Hypothesis 2 (Non-directional): There will be a difference in information retention between students who listen to music while studying and those who study in silence.

How can ResearchProspect Help?

If you are unsure about how to rest a research hypothesis in a dissertation or simply unsure about how to develop a hypothesis for your research, then you can take advantage of our dissertation services which cover every tiny aspect of a dissertation project you might need help with including but not limited to setting up a hypothesis and research questions,  help with individual chapters ,  full dissertation writing ,  statistical analysis , and much more.

Frequently Asked Questions

What are the 5 rules for writing a good hypothesis.

• Clear Statement: State a clear relationship between variables.
• Testable: Ensure it can be investigated and measured.
• Specific: Avoid vague terms, be precise in predictions.
• Falsifiable: Design to allow potential disproof.
• Relevant: Address research question and align with existing knowledge.

What is a hypothesis in simple words?

A hypothesis is an educated guess or prediction about something that can be tested. It is a statement that suggests a possible explanation for an event or phenomenon based on prior knowledge or observation. Scientists use hypotheses as a starting point for experiments to discover if they are true or false.

What is the hypothesis and examples?

A hypothesis is a testable prediction or explanation for an observation or phenomenon. For example, if plants are given sunlight, then they will grow. In this case, the hypothesis suggests that sunlight has a positive effect on plant growth. It can be tested by experimenting with plants in varying light conditions.

What is the hypothesis in research definition?

A hypothesis in research is a clear, testable statement predicting the possible outcome of a study based on prior knowledge and observation. It serves as the foundation for conducting experiments or investigations. Researchers test the validity of the hypothesis to draw conclusions and advance knowledge in a particular field.

Why is it called a hypothesis?

The term “hypothesis” originates from the Greek word “hypothesis,” which means “base” or “foundation.” It’s used to describe a foundational statement or proposition that can be tested. In scientific contexts, it denotes a tentative explanation for a phenomenon, serving as a starting point for investigation or experimentation.

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How to Write a Research Hypothesis: Good & Bad Examples

What is a research hypothesis?

A research hypothesis is an attempt at explaining a phenomenon or the relationships between phenomena/variables in the real world. Hypotheses are sometimes called “educated guesses”, but they are in fact (or let’s say they should be) based on previous observations, existing theories, scientific evidence, and logic. A research hypothesis is also not a prediction—rather, predictions are ( should be) based on clearly formulated hypotheses. For example, “We tested the hypothesis that KLF2 knockout mice would show deficiencies in heart development” is an assumption or prediction, not a hypothesis. 

The research hypothesis at the basis of this prediction is “the product of the KLF2 gene is involved in the development of the cardiovascular system in mice”—and this hypothesis is probably (hopefully) based on a clear observation, such as that mice with low levels of Kruppel-like factor 2 (which KLF2 codes for) seem to have heart problems. From this hypothesis, you can derive the idea that a mouse in which this particular gene does not function cannot develop a normal cardiovascular system, and then make the prediction that we started with. 

What is the difference between a hypothesis and a prediction?

You might think that these are very subtle differences, and you will certainly come across many publications that do not contain an actual hypothesis or do not make these distinctions correctly. But considering that the formulation and testing of hypotheses is an integral part of the scientific method, it is good to be aware of the concepts underlying this approach. The two hallmarks of a scientific hypothesis are falsifiability (an evaluation standard that was introduced by the philosopher of science Karl Popper in 1934) and testability —if you cannot use experiments or data to decide whether an idea is true or false, then it is not a hypothesis (or at least a very bad one).

So, in a nutshell, you (1) look at existing evidence/theories, (2) come up with a hypothesis, (3) make a prediction that allows you to (4) design an experiment or data analysis to test it, and (5) come to a conclusion. Of course, not all studies have hypotheses (there is also exploratory or hypothesis-generating research), and you do not necessarily have to state your hypothesis as such in your paper. 

But for the sake of understanding the principles of the scientific method, let’s first take a closer look at the different types of hypotheses that research articles refer to and then give you a step-by-step guide for how to formulate a strong hypothesis for your own paper.

Types of Research Hypotheses

Hypotheses can be simple , which means they describe the relationship between one single independent variable (the one you observe variations in or plan to manipulate) and one single dependent variable (the one you expect to be affected by the variations/manipulation). If there are more variables on either side, you are dealing with a complex hypothesis. You can also distinguish hypotheses according to the kind of relationship between the variables you are interested in (e.g., causal or associative ). But apart from these variations, we are usually interested in what is called the “alternative hypothesis” and, in contrast to that, the “null hypothesis”. If you think these two should be listed the other way round, then you are right, logically speaking—the alternative should surely come second. However, since this is the hypothesis we (as researchers) are usually interested in, let’s start from there.

Alternative Hypothesis

If you predict a relationship between two variables in your study, then the research hypothesis that you formulate to describe that relationship is your alternative hypothesis (usually H1 in statistical terms). The goal of your hypothesis testing is thus to demonstrate that there is sufficient evidence that supports the alternative hypothesis, rather than evidence for the possibility that there is no such relationship. The alternative hypothesis is usually the research hypothesis of a study and is based on the literature, previous observations, and widely known theories. 

Null Hypothesis

The hypothesis that describes the other possible outcome, that is, that your variables are not related, is the null hypothesis ( H0 ). Based on your findings, you choose between the two hypotheses—usually that means that if your prediction was correct, you reject the null hypothesis and accept the alternative. Make sure, however, that you are not getting lost at this step of the thinking process: If your prediction is that there will be no difference or change, then you are trying to find support for the null hypothesis and reject H1. 

Directional Hypothesis

While the null hypothesis is obviously “static”, the alternative hypothesis can specify a direction for the observed relationship between variables—for example, that mice with higher expression levels of a certain protein are more active than those with lower levels. This is then called a one-tailed hypothesis. 

Another example for a directional one-tailed alternative hypothesis would be that 

H1: Attending private classes before important exams has a positive effect on performance. 

Your null hypothesis would then be that

H0: Attending private classes before important exams has no/a negative effect on performance.

Nondirectional Hypothesis

A nondirectional hypothesis does not specify the direction of the potentially observed effect, only that there is a relationship between the studied variables—this is called a two-tailed hypothesis. For instance, if you are studying a new drug that has shown some effects on pathways involved in a certain condition (e.g., anxiety) in vitro in the lab, but you can’t say for sure whether it will have the same effects in an animal model or maybe induce other/side effects that you can’t predict and potentially increase anxiety levels instead, you could state the two hypotheses like this:

H1: The only lab-tested drug (somehow) affects anxiety levels in an anxiety mouse model.

You then test this nondirectional alternative hypothesis against the null hypothesis:

H0: The only lab-tested drug has no effect on anxiety levels in an anxiety mouse model.

How to Write a Hypothesis for a Research Paper

Now that we understand the important distinctions between different kinds of research hypotheses, let’s look at a simple process of how to write a hypothesis.

Writing a Hypothesis Step:1

Ask a question, based on earlier research. Research always starts with a question, but one that takes into account what is already known about a topic or phenomenon. For example, if you are interested in whether people who have pets are happier than those who don’t, do a literature search and find out what has already been demonstrated. You will probably realize that yes, there is quite a bit of research that shows a relationship between happiness and owning a pet—and even studies that show that owning a dog is more beneficial than owning a cat ! Let’s say you are so intrigued by this finding that you wonder: 

What is it that makes dog owners even happier than cat owners? 

Let’s move on to Step 2 and find an answer to that question.

Writing a Hypothesis Step 2:

Formulate a strong hypothesis by answering your own question. Again, you don’t want to make things up, take unicorns into account, or repeat/ignore what has already been done. Looking at the dog-vs-cat papers your literature search returned, you see that most studies are based on self-report questionnaires on personality traits, mental health, and life satisfaction. What you don’t find is any data on actual (mental or physical) health measures, and no experiments. You therefore decide to make a bold claim come up with the carefully thought-through hypothesis that it’s maybe the lifestyle of the dog owners, which includes walking their dog several times per day, engaging in fun and healthy activities such as agility competitions, and taking them on trips, that gives them that extra boost in happiness. You could therefore answer your question in the following way:

Dog owners are happier than cat owners because of the dog-related activities they engage in.

Now you have to verify that your hypothesis fulfills the two requirements we introduced at the beginning of this resource article: falsifiability and testability . If it can’t be wrong and can’t be tested, it’s not a hypothesis. We are lucky, however, because yes, we can test whether owning a dog but not engaging in any of those activities leads to lower levels of happiness or well-being than owning a dog and playing and running around with them or taking them on trips.  

Writing a Hypothesis Step 3:

Make your predictions and define your variables. We have verified that we can test our hypothesis, but now we have to define all the relevant variables, design our experiment or data analysis, and make precise predictions. You could, for example, decide to study dog owners (not surprising at this point), let them fill in questionnaires about their lifestyle as well as their life satisfaction (as other studies did), and then compare two groups of active and inactive dog owners. Alternatively, if you want to go beyond the data that earlier studies produced and analyzed and directly manipulate the activity level of your dog owners to study the effect of that manipulation, you could invite them to your lab, select groups of participants with similar lifestyles, make them change their lifestyle (e.g., couch potato dog owners start agility classes, very active ones have to refrain from any fun activities for a certain period of time) and assess their happiness levels before and after the intervention. In both cases, your independent variable would be “ level of engagement in fun activities with dog” and your dependent variable would be happiness or well-being . 

Examples of a Good and Bad Hypothesis

Let’s look at a few examples of good and bad hypotheses to get you started.

Good Hypothesis Examples

Bad hypothesis examples, tips for writing a research hypothesis.

If you understood the distinction between a hypothesis and a prediction we made at the beginning of this article, then you will have no problem formulating your hypotheses and predictions correctly. To refresh your memory: We have to (1) look at existing evidence, (2) come up with a hypothesis, (3) make a prediction, and (4) design an experiment. For example, you could summarize your dog/happiness study like this:

(1) While research suggests that dog owners are happier than cat owners, there are no reports on what factors drive this difference. (2) We hypothesized that it is the fun activities that many dog owners (but very few cat owners) engage in with their pets that increases their happiness levels. (3) We thus predicted that preventing very active dog owners from engaging in such activities for some time and making very inactive dog owners take up such activities would lead to an increase and decrease in their overall self-ratings of happiness, respectively. (4) To test this, we invited dog owners into our lab, assessed their mental and emotional well-being through questionnaires, and then assigned them to an “active” and an “inactive” group, depending on… 

Note that you use “we hypothesize” only for your hypothesis, not for your experimental prediction, and “would” or “if – then” only for your prediction, not your hypothesis. A hypothesis that states that something “would” affect something else sounds as if you don’t have enough confidence to make a clear statement—in which case you can’t expect your readers to believe in your research either. Write in the present tense, don’t use modal verbs that express varying degrees of certainty (such as may, might, or could ), and remember that you are not drawing a conclusion while trying not to exaggerate but making a clear statement that you then, in a way, try to disprove . And if that happens, that is not something to fear but an important part of the scientific process.

Similarly, don’t use “we hypothesize” when you explain the implications of your research or make predictions in the conclusion section of your manuscript, since these are clearly not hypotheses in the true sense of the word. As we said earlier, you will find that many authors of academic articles do not seem to care too much about these rather subtle distinctions, but thinking very clearly about your own research will not only help you write better but also ensure that even that infamous Reviewer 2 will find fewer reasons to nitpick about your manuscript. 

Perfect Your Manuscript With Professional Editing

Now that you know how to write a strong research hypothesis for your research paper, you might be interested in our free AI proofreader , Wordvice AI, which finds and fixes errors in grammar, punctuation, and word choice in academic texts. Or if you are interested in human proofreading , check out our English editing services , including research paper editing and manuscript editing .

On the Wordvice academic resources website , you can also find many more articles and other resources that can help you with writing the other parts of your research paper , with making a research paper outline before you put everything together, or with writing an effective cover letter once you are ready to submit.

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• v.36(50); 2021 Dec 27

Formulating Hypotheses for Different Study Designs

Durga prasanna misra.

1 Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, India.

Armen Yuri Gasparyan

2 Departments of Rheumatology and Research and Development, Dudley Group NHS Foundation Trust (Teaching Trust of the University of Birmingham, UK), Russells Hall Hospital, Dudley, UK.

Olena Zimba

3 Department of Internal Medicine #2, Danylo Halytsky Lviv National Medical University, Lviv, Ukraine.

Marlen Yessirkepov

4 Department of Biology and Biochemistry, South Kazakhstan Medical Academy, Shymkent, Kazakhstan.

Vikas Agarwal

George d. kitas.

5 Centre for Epidemiology versus Arthritis, University of Manchester, Manchester, UK.

Generating a testable working hypothesis is the first step towards conducting original research. Such research may prove or disprove the proposed hypothesis. Case reports, case series, online surveys and other observational studies, clinical trials, and narrative reviews help to generate hypotheses. Observational and interventional studies help to test hypotheses. A good hypothesis is usually based on previous evidence-based reports. Hypotheses without evidence-based justification and a priori ideas are not received favourably by the scientific community. Original research to test a hypothesis should be carefully planned to ensure appropriate methodology and adequate statistical power. While hypotheses can challenge conventional thinking and may be controversial, they should not be destructive. A hypothesis should be tested by ethically sound experiments with meaningful ethical and clinical implications. The coronavirus disease 2019 pandemic has brought into sharp focus numerous hypotheses, some of which were proven (e.g. effectiveness of corticosteroids in those with hypoxia) while others were disproven (e.g. ineffectiveness of hydroxychloroquine and ivermectin).

Graphical Abstract

DEFINING WORKING AND STANDALONE SCIENTIFIC HYPOTHESES

Science is the systematized description of natural truths and facts. Routine observations of existing life phenomena lead to the creative thinking and generation of ideas about mechanisms of such phenomena and related human interventions. Such ideas presented in a structured format can be viewed as hypotheses. After generating a hypothesis, it is necessary to test it to prove its validity. Thus, hypothesis can be defined as a proposed mechanism of a naturally occurring event or a proposed outcome of an intervention. 1 , 2

Hypothesis testing requires choosing the most appropriate methodology and adequately powering statistically the study to be able to “prove” or “disprove” it within predetermined and widely accepted levels of certainty. This entails sample size calculation that often takes into account previously published observations and pilot studies. 2 , 3 In the era of digitization, hypothesis generation and testing may benefit from the availability of numerous platforms for data dissemination, social networking, and expert validation. Related expert evaluations may reveal strengths and limitations of proposed ideas at early stages of post-publication promotion, preventing the implementation of unsupported controversial points. 4

Thus, hypothesis generation is an important initial step in the research workflow, reflecting accumulating evidence and experts' stance. In this article, we overview the genesis and importance of scientific hypotheses and their relevance in the era of the coronavirus disease 2019 (COVID-19) pandemic.

DO WE NEED HYPOTHESES FOR ALL STUDY DESIGNS?

Broadly, research can be categorized as primary or secondary. In the context of medicine, primary research may include real-life observations of disease presentations and outcomes. Single case descriptions, which often lead to new ideas and hypotheses, serve as important starting points or justifications for case series and cohort studies. The importance of case descriptions is particularly evident in the context of the COVID-19 pandemic when unique, educational case reports have heralded a new era in clinical medicine. 5

Case series serve similar purpose to single case reports, but are based on a slightly larger quantum of information. Observational studies, including online surveys, describe the existing phenomena at a larger scale, often involving various control groups. Observational studies include variable-scale epidemiological investigations at different time points. Interventional studies detail the results of therapeutic interventions.

Secondary research is based on already published literature and does not directly involve human or animal subjects. Review articles are generated by secondary research. These could be systematic reviews which follow methods akin to primary research but with the unit of study being published papers rather than humans or animals. Systematic reviews have a rigid structure with a mandatory search strategy encompassing multiple databases, systematic screening of search results against pre-defined inclusion and exclusion criteria, critical appraisal of study quality and an optional component of collating results across studies quantitatively to derive summary estimates (meta-analysis). 6 Narrative reviews, on the other hand, have a more flexible structure. Systematic literature searches to minimise bias in selection of articles are highly recommended but not mandatory. 7 Narrative reviews are influenced by the authors' viewpoint who may preferentially analyse selected sets of articles. 8

In relation to primary research, case studies and case series are generally not driven by a working hypothesis. Rather, they serve as a basis to generate a hypothesis. Observational or interventional studies should have a hypothesis for choosing research design and sample size. The results of observational and interventional studies further lead to the generation of new hypotheses, testing of which forms the basis of future studies. Review articles, on the other hand, may not be hypothesis-driven, but form fertile ground to generate future hypotheses for evaluation. Fig. 1 summarizes which type of studies are hypothesis-driven and which lead on to hypothesis generation.

STANDARDS OF WORKING AND SCIENTIFIC HYPOTHESES

A review of the published literature did not enable the identification of clearly defined standards for working and scientific hypotheses. It is essential to distinguish influential versus not influential hypotheses, evidence-based hypotheses versus a priori statements and ideas, ethical versus unethical, or potentially harmful ideas. The following points are proposed for consideration while generating working and scientific hypotheses. 1 , 2 Table 1 summarizes these points.

Evidence-based data

A scientific hypothesis should have a sound basis on previously published literature as well as the scientist's observations. Randomly generated (a priori) hypotheses are unlikely to be proven. A thorough literature search should form the basis of a hypothesis based on published evidence. 7

Unless a scientific hypothesis can be tested, it can neither be proven nor be disproven. Therefore, a scientific hypothesis should be amenable to testing with the available technologies and the present understanding of science.

Supported by pilot studies

If a hypothesis is based purely on a novel observation by the scientist in question, it should be grounded on some preliminary studies to support it. For example, if a drug that targets a specific cell population is hypothesized to be useful in a particular disease setting, then there must be some preliminary evidence that the specific cell population plays a role in driving that disease process.

Testable by ethical studies

The hypothesis should be testable by experiments that are ethically acceptable. 9 For example, a hypothesis that parachutes reduce mortality from falls from an airplane cannot be tested using a randomized controlled trial. 10 This is because it is obvious that all those jumping from a flying plane without a parachute would likely die. Similarly, the hypothesis that smoking tobacco causes lung cancer cannot be tested by a clinical trial that makes people take up smoking (since there is considerable evidence for the health hazards associated with smoking). Instead, long-term observational studies comparing outcomes in those who smoke and those who do not, as was performed in the landmark epidemiological case control study by Doll and Hill, 11 are more ethical and practical.

Balance between scientific temper and controversy

Novel findings, including novel hypotheses, particularly those that challenge established norms, are bound to face resistance for their wider acceptance. Such resistance is inevitable until the time such findings are proven with appropriate scientific rigor. However, hypotheses that generate controversy are generally unwelcome. For example, at the time the pandemic of human immunodeficiency virus (HIV) and AIDS was taking foot, there were numerous deniers that refused to believe that HIV caused AIDS. 12 , 13 Similarly, at a time when climate change is causing catastrophic changes to weather patterns worldwide, denial that climate change is occurring and consequent attempts to block climate change are certainly unwelcome. 14 The denialism and misinformation during the COVID-19 pandemic, including unfortunate examples of vaccine hesitancy, are more recent examples of controversial hypotheses not backed by science. 15 , 16 An example of a controversial hypothesis that was a revolutionary scientific breakthrough was the hypothesis put forth by Warren and Marshall that Helicobacter pylori causes peptic ulcers. Initially, the hypothesis that a microorganism could cause gastritis and gastric ulcers faced immense resistance. When the scientists that proposed the hypothesis themselves ingested H. pylori to induce gastritis in themselves, only then could they convince the wider world about their hypothesis. Such was the impact of the hypothesis was that Barry Marshall and Robin Warren were awarded the Nobel Prize in Physiology or Medicine in 2005 for this discovery. 17 , 18

DISTINGUISHING THE MOST INFLUENTIAL HYPOTHESES

Influential hypotheses are those that have stood the test of time. An archetype of an influential hypothesis is that proposed by Edward Jenner in the eighteenth century that cowpox infection protects against smallpox. While this observation had been reported for nearly a century before this time, it had not been suitably tested and publicised until Jenner conducted his experiments on a young boy by demonstrating protection against smallpox after inoculation with cowpox. 19 These experiments were the basis for widespread smallpox immunization strategies worldwide in the 20th century which resulted in the elimination of smallpox as a human disease today. 20

Other influential hypotheses are those which have been read and cited widely. An example of this is the hygiene hypothesis proposing an inverse relationship between infections in early life and allergies or autoimmunity in adulthood. An analysis reported that this hypothesis had been cited more than 3,000 times on Scopus. 1

LESSONS LEARNED FROM HYPOTHESES AMIDST THE COVID-19 PANDEMIC

The COVID-19 pandemic devastated the world like no other in recent memory. During this period, various hypotheses emerged, understandably so considering the public health emergency situation with innumerable deaths and suffering for humanity. Within weeks of the first reports of COVID-19, aberrant immune system activation was identified as a key driver of organ dysfunction and mortality in this disease. 21 Consequently, numerous drugs that suppress the immune system or abrogate the activation of the immune system were hypothesized to have a role in COVID-19. 22 One of the earliest drugs hypothesized to have a benefit was hydroxychloroquine. Hydroxychloroquine was proposed to interfere with Toll-like receptor activation and consequently ameliorate the aberrant immune system activation leading to pathology in COVID-19. 22 The drug was also hypothesized to have a prophylactic role in preventing infection or disease severity in COVID-19. It was also touted as a wonder drug for the disease by many prominent international figures. However, later studies which were well-designed randomized controlled trials failed to demonstrate any benefit of hydroxychloroquine in COVID-19. 23 , 24 , 25 , 26 Subsequently, azithromycin 27 , 28 and ivermectin 29 were hypothesized as potential therapies for COVID-19, but were not supported by evidence from randomized controlled trials. The role of vitamin D in preventing disease severity was also proposed, but has not been proven definitively until now. 30 , 31 On the other hand, randomized controlled trials identified the evidence supporting dexamethasone 32 and interleukin-6 pathway blockade with tocilizumab as effective therapies for COVID-19 in specific situations such as at the onset of hypoxia. 33 , 34 Clues towards the apparent effectiveness of various drugs against severe acute respiratory syndrome coronavirus 2 in vitro but their ineffectiveness in vivo have recently been identified. Many of these drugs are weak, lipophilic bases and some others induce phospholipidosis which results in apparent in vitro effectiveness due to non-specific off-target effects that are not replicated inside living systems. 35 , 36

Another hypothesis proposed was the association of the routine policy of vaccination with Bacillus Calmette-Guerin (BCG) with lower deaths due to COVID-19. This hypothesis emerged in the middle of 2020 when COVID-19 was still taking foot in many parts of the world. 37 , 38 Subsequently, many countries which had lower deaths at that time point went on to have higher numbers of mortality, comparable to other areas of the world. Furthermore, the hypothesis that BCG vaccination reduced COVID-19 mortality was a classic example of ecological fallacy. Associations between population level events (ecological studies; in this case, BCG vaccination and COVID-19 mortality) cannot be directly extrapolated to the individual level. Furthermore, such associations cannot per se be attributed as causal in nature, and can only serve to generate hypotheses that need to be tested at the individual level. 39

IS TRADITIONAL PEER REVIEW EFFICIENT FOR EVALUATION OF WORKING AND SCIENTIFIC HYPOTHESES?

Traditionally, publication after peer review has been considered the gold standard before any new idea finds acceptability amongst the scientific community. Getting a work (including a working or scientific hypothesis) reviewed by experts in the field before experiments are conducted to prove or disprove it helps to refine the idea further as well as improve the experiments planned to test the hypothesis. 40 A route towards this has been the emergence of journals dedicated to publishing hypotheses such as the Central Asian Journal of Medical Hypotheses and Ethics. 41 Another means of publishing hypotheses is through registered research protocols detailing the background, hypothesis, and methodology of a particular study. If such protocols are published after peer review, then the journal commits to publishing the completed study irrespective of whether the study hypothesis is proven or disproven. 42 In the post-pandemic world, online research methods such as online surveys powered via social media channels such as Twitter and Instagram might serve as critical tools to generate as well as to preliminarily test the appropriateness of hypotheses for further evaluation. 43 , 44

Some radical hypotheses might be difficult to publish after traditional peer review. These hypotheses might only be acceptable by the scientific community after they are tested in research studies. Preprints might be a way to disseminate such controversial and ground-breaking hypotheses. 45 However, scientists might prefer to keep their hypotheses confidential for the fear of plagiarism of ideas, avoiding online posting and publishing until they have tested the hypotheses.

SUGGESTIONS ON GENERATING AND PUBLISHING HYPOTHESES

Publication of hypotheses is important, however, a balance is required between scientific temper and controversy. Journal editors and reviewers might keep in mind these specific points, summarized in Table 2 and detailed hereafter, while judging the merit of hypotheses for publication. Keeping in mind the ethical principle of primum non nocere, a hypothesis should be published only if it is testable in a manner that is ethically appropriate. 46 Such hypotheses should be grounded in reality and lend themselves to further testing to either prove or disprove them. It must be considered that subsequent experiments to prove or disprove a hypothesis have an equal chance of failing or succeeding, akin to tossing a coin. A pre-conceived belief that a hypothesis is unlikely to be proven correct should not form the basis of rejection of such a hypothesis for publication. In this context, hypotheses generated after a thorough literature search to identify knowledge gaps or based on concrete clinical observations on a considerable number of patients (as opposed to random observations on a few patients) are more likely to be acceptable for publication by peer-reviewed journals. Also, hypotheses should be considered for publication or rejection based on their implications for science at large rather than whether the subsequent experiments to test them end up with results in favour of or against the original hypothesis.

Hypotheses form an important part of the scientific literature. The COVID-19 pandemic has reiterated the importance and relevance of hypotheses for dealing with public health emergencies and highlighted the need for evidence-based and ethical hypotheses. A good hypothesis is testable in a relevant study design, backed by preliminary evidence, and has positive ethical and clinical implications. General medical journals might consider publishing hypotheses as a specific article type to enable more rapid advancement of science.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Data curation: Gasparyan AY, Misra DP, Zimba O, Yessirkepov M, Agarwal V, Kitas GD.

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• Published: 13 May 2024

The effects of social support and parental autonomy support on the mental well-being of university students: the mediating role of a parent–child relationship

• Arif Jameel 1 ,
• Zhiqiang Ma 1 ,
• Mingxing Li 1 , 2 ,
• Abid Hussain 1 ,
• Muhammad Asif 1 &
• Yan Wang 1  

Humanities and Social Sciences Communications volume  11 , Article number:  622 ( 2024 ) Cite this article

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The COVID-19 pandemic, as well as the complex response techniques employed to halt its spread, are both detrimental to mental and emotional well-being. Students’ lives have been damaged by social alienation and self-isolation. These effects must be detected, analyzed, and dealt with to make sure the well-being of individuals, specifically students. This research examines the impact of parent–child relationships, parental autonomy support, and social support on enhancing students’ mental well-being using data collected from post-COVID-19. The Potential participants were students from several universities in Pakistan. For this reason, we chose Pakistan’s Punjab province, with 8 prominent institutions, as the primary focus for data collection. A questionnaire was created to gather information from 355 students. For descriptive statistics, SPSS was used, while AMOS structural equation modeling was used to test hypotheses. The findings revealed that social support on mental well-being (standardized β  = 0.43, t  = 7.57, p  < 0.01) and parental autonomy support was significant and positively related to mental well-being (standardized β  = 0.31, t  = 5.016, p  < 0.01), and predicted parent–child relationships. Furthermore, the parent–child relationship strongly mediated the association between social support, parental autonomy support, and students’ mental well-being. This research proposes that good social support and parental autonomy support improve parent–children relationships and contribute to students’ mental well-being.

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Introduction.

COVID-19 has grown increasingly concerned with mental health and well-being in the past few years. Many research investigations have found that students have higher levels of psychological suffering than the overall people. This psychological tension of this virus among students has had significant and longer-term mental health repercussions, leading to low physical well-being results, including an increase in cardiovascular illnesses and lowly mental health (MH) results. Students suffer from the mental load of this impact more than grownups because they lack the grownup’s ways to cope and physical growth (Rawat and Sehrawat, 2021 ). Students who have a history of MH difficulties are more likely to suffer MH problems amid a crisis (Gavin et al., 2020 ). On the advice of the Emergency Committee, the head of the “World Health Organization (WHO)” stated the novel Coronavirus, also identified as “COVID-19”, is a Health Emergency of Worldwide Distress. COVID-19 has catastrophic impacts on the global business environment, schooling, and humanity (Priya et al., 2021 ). Health professionals designed a complex response plan to stem the spread of COVID-19 from the pandemic’s start. Being isolated or home quarantined was an essential part of the approach. One of the measures authorities have attempted to sluggish the spread of the virus is isolation from society. Isolation from society can affect mental health, increasing symptoms of anxiety, depression, and stress (Robb et al., 2020 ). There has been a surge in the number of students reporting mental health difficulties in the present years at colleges and universities. The underlying explanation might be university students’ inexperience, as they often struggle to handle stress, mainly when confronted with educational, social, and career-related challenges. Following research, the COVID-19 pandemic increases students’ chances of developing unhappiness and suicidality (Xiong et al., 2021 ). Following the closure of university campuses, students tended to see their educational future as bleak. Isolation from society and a lack of adequate and effective MH assistance exacerbated students’ fragile status. Because of these factors, young individuals pursuing university are now at a higher risk of acquiring MH disorders (Su et al., 2021 ).

Social support is instinctively understood, but ideas about definitions conflict when specific questions are raised. Family practitioners believe that social support is one of the possible keys to an individual’s well-being, especially for those going through significant life transitions or crises (Kaplan et al., 1977 ). The definition of “social support” varies usually among those who have studied it. It has been discussed in a general way as support that is “provided by other people and arises within the context of interpersonal relationships” Cooke et al. ( 1988 ) and as “support accessible to an individual through social ties to other individuals, groups, and the larger community” (Lin et al., 1979 ). Parent–children relations are interpersonal interactions formed by the interaction of parents and Child in blood and genetically related families. Parent–children ties are the first social associations to which individuals are exposed. It influences many facets of personality development, social cognition, and mental well-being (Lu et al., 2020 ). A lower degree of social support, in particular, is connected to greater levels of depressive symptoms (Wang and Peck, 2013 ). Social support refers to the standard of emotional assistance provided by others.

Furthermore, research shows that social support levels are closely related to measures of reduced stress and psychological discomfort, as well as improved well-being (Wang and Peck, 2013 ). Nevertheless, most research on youths’ social support focuses on their families, with relatively little research on their peers’ social support (Oktavia et al., 2019 ). Based on the gaps in existing knowledge, this research intended to determine whether there is a link between parent–child relationships during social seclusion caused by the pandemic and MH. This study additionally explored how social support (SS), parental autonomy support (PAS), and parent–child relationships are related to students’ mental health and well-being. It also looks into the role of the parent–child relationship as a mediator. The outcomes of this investigation are expected to increase understanding of the topic. Despite several research in the field of MH, there is still a literature gap on the roles and linkages of parent–child relationships and their mediator behaviors. This research provides a model for simultaneously investigating the roles and intervening factors. The focus of this investigation is on the following research questions. How can social and parental autonomy support affect students’ MH following COVID-19? How does the parent–children relationship mediate this relationship? Following an exhaustive assessment of the pertinent literature (Akram et al., 2022 ; Li et al., 2022 ), it was discovered that numerous research has been conducted to evaluate parents–children relationships and MH-associated issues, but to the best of our knowledge no research has been performed in Pakistan yet to determine the answers to these study topics will be provided utilizing a single theoretical model. Based on the available literature, the present study initially posits that social and parental autonomy support favorably promotes the student’s mental well-being and positively connects with a parent–child relationship in Pakistan. The study also hypothesizes that the parent–child relationship impacts the mental well-being of the students and mediates the connection between social support, parental autonomy support, and mental well-being. The research could have subsequent contributions: To begin, the research provides a detailed and systematic investigation of the concepts of the parents–children relationship, social support, parental autonomy support, and mental well-being. Second, by integrating the parents–children relationship, the study enhances the comprehensive analytical model that investigates the association between SS, PAS, and mental well-being. The model of the research describes the theoretical viewpoint in an innovative manner. Furthermore, the work has both practical and theoretical ramifications.

Literature review

Social support and mental well-being of students.

Social support is instinctively understood, but ideas about definitions conflict when specific questions are raised. Family practitioners believe that social support is one of the possible keys to an individual’s well-being, especially for those going through significant life transitions or crises (Kaplan et al., 1977 ; Wilcox and Vernberg, 1985 ). The definition of “social support” varies usually among those who have studied it. It has been discussed in a general way as support that is “provided by other people and arises within the context of interpersonal relationships” Cooke et al. ( 1988 ) and as “support accessible to an individual through social ties to other individuals, groups, and the larger community” (Lin et al., 1979 ).

Multiple research investigations have revealed that several internal elements influence young students’ mental well-being, notably the temperament of the students Ypsilanti et al. ( 2020 ), parental style Rinaldi and Howe ( 2012 ), and peer interaction (Holmes et al., 2016 ). One of the most significant macrosocial elements impacting students’ mental health is social support, which relates to the subjective and objective support they get from their circle of friends and how they utilize it (Shen, 2009 ). “Family support, friend support, and other support” are common sources of social support (Dahlem et al., 1991 , p. 760). Through social bonding, social support may reduce psychological stress and maintain or enhance a person’s mental and physical well-being (Cohen and McKay, 2020 ; Tao et al., 2022 ). Prior studies have found that social support can make parents more positive, enhance their mental and physical wellness, and improve their parenting efficacy (Yan et al., 2023 ). Once parents believe they have access to support and networks of friends, their psychological well-being rises (Chatters et al., 2015 ). Parents with higher social support are more nurturing and consistent in their parenting and less likely to use harsh parenting behaviors across a range of child ages Byrnes and Miller ( 2012 ), and social support may assist parents in managing how they react emotionally to their kids (Marroquín, 2011 ). Social support may also give parents developmental knowledge and advice on proper parenting practices, allowing them to adapt to their expectations and enhance their parenting abilities (Ayala-Nunes et al., 2017 ). A lack of or insufficient social support, on the other hand, maybe an indicator of risk for parental psychological wellness, leading to incorrect parenting behaviors (Belsky and Jaffee, 2015 ; Hu et al., 2023 ). Parents with psychological problems have fewer beneficial relationships with their kids, experience more instances of not positive interactions and enmity, express less efficiently, and are less responsive to their children’s actions (Herwig et al., 2004 ). As a result, parents’ perceived social support influences parenting ideas and conduct, which can impact children’s mental well-being development. As a result, parents’ perceived social support may be favorably related to the mental well-being of their children.

H1: Social support positively related to the mental well-being of students

Parental autonomy support and mental well-being of students

Following the self-determination theory Ryan and Deci ( 2000 ), “autonomy” is the fundamental cognitive or emotional need that leads to optimum growth and functioning, for instance, higher levels of educational accomplishment and improved psychological well-being of students (Vasquez et al., 2016 ). Parental support has been proven in studies to increase autonomy in young people (Inguglia et al., 2015 ). Parental autonomy support (PAS) refers to parents promoting emerging adolescents’ growing desires for independence, like liberty of expression, pondering, and making decisions (Soenens et al., 2007 ). Numerous research concentrating on European societies have found that parental autonomy support is connected with positive psychosocial adjustment in individuals (Froiland, 2011 ; Soenens et al., 2007 ). An empirical study, for example, has shown that autonomy support in intimate associations is an important predictor of mental well-being (Arslan and Asıcı, 2022 ; Shamir and Shamir Balderman, 2023 ). Likewise, Kins et al. ( 2009 ) found that PAS is related to greater mental well-being in Belgian young adults. Surprisingly, cross-cultural research found that parental autonomy support is connected to mental well-being in “Chinese and North American” teenagers Lekes et al. ( 2010 ), indicating that PAS benefits people working in a group environment.

Furthermore, according to the latest meta-analysis, the parental autonomy support association is greater when it reflects both parents instead of just moms and dads (Vasquez et al., 2016 ). Accepting this viewpoint, the present research emphasizes PAS. Whereas various research in Western cultures indicates the relationship between PAS and mental well-being, nothing is known about the advantages of “parental autonomy support” in a communal community or the fundamental connection between PAS and mental well-being. Through self-regulatory processes, culture can influence mental well-being, impacting how individuals think, feel, and conduct themselves in pursuit of mental well-being (Siu, Spector, Cooper, and Lu, 2005 ). Thus, we posit that PAS impacts the mental well-being of university students.

H2: Parental autonomy support positively related to the mental well-being of the students

Mediating effect of parent–children relationship

Parent–children connections are interpersonal interactions formed by the interaction of parents and Child in blood and genetically related families. Parent–children ties are the first social associations to which individuals are exposed. It influences many facets of personality development, social cognition, and mental well-being (Lu et al., 2020 ). Greater social interaction has been shown to improve parent–children interactions, increase parent–children warmth, and decrease parent–children animosity (Lippold et al., 2018 ). This might be attributed to two factors. On the one hand, social assistance may significantly enhance children’s quality of family life (Balcells-Balcells et al., 2019 ; Feng et al., 2022 ). Parents might have more time to dedicate to parenting, resulting in improved parent–children interactions. On the other hand, social support has been shown to lower parental stress, promote mental well-being, and favorably affect how parents act (Avila et al., 2015 ; Östberg and Hagekull, 2000 ). Social support can help parents get good parenting counsel and assistance (Dominguez and Watkins, 2003 ). Social support may assist parents in managing their feelings about their children, which leads to improved parenting practices and more parental warmth (Byrnes and Miller, 2012 ). Parent–children relationships and children’s mental well-being are inextricably linked. Parent–children connections are crucial in the development of children. Parent–children connections have a greater influence on the Child than other interpersonal interactions in the family and have a significant impact on the growth of a person’s personality, mental well-being, and adjustment (Nock et al., 2009 ).

Parent–children attachment and intimacy are significant manifestations of parent–children interactions. In the long run, the continuing emotional link between a kid and a caregiver is known as parent–children bonding. A strong bond is a vital basis for children’s healthy development and integration into society, and parent–children bonds remain stable as adolescents age (Juffer et al., 2012 ). It has been demonstrated that young people with solid parent–children bonds acquire more beneficial social abilities, have greater cognitive functioning, and have greater mental and physical wellness (Ranson and Urichuk, 2008 ). Parent–children attachment is the tight, warm relationship between parents and kids, which may be shown in positive interaction behaviors and close sentiments about one another (Chen et al., 2015 ). According to several types of research, the parent–children connection is the foundation of proper child development and the most consistent safeguard for healthy personal growth (Barber et al., 2005 ). Li et al. ( 2022 ) used the parent–child relationship as a mediator in their study to explore the impact of parental mediation on internet addiction. In a nutshell, students who have close, warm parent–children connections experience less externalizing and internalizing difficulties Lamborn and Felbab ( 2003 ), have a lower incidence of suicide ideation Harris and Molock ( 2000 ), and have improved psychological well-being. Thus, parent–children relationships may act as a mediating variable between SS, PAS, and the mental well-being of students.

H3: Parents–children relationship mediates the association between social support and the mental well-being of the students

H4: Parents–children relationship mediates the association between parental autonomy support and the mental well-being of the students

Figure 1 depicts the hypothesized study model. The direct impacts of SS and PAS on the mental well-being of university students were investigated first, followed by studying the other linkages and indirect effects among social support, parental autonomy support, parent–children relationships, and the mental well-being of the students.

Hypothesized model.

Materials and methods

Sampling technique and data collection.

The Potential participants were students from several universities in Pakistan. For this reason, we chose Pakistan’s Punjab province, with 8 prominent institutions as the primary focus. Due to Covid 19, it was projected that the majority of the students would remain at home and endure some form of psychological disorder with their families. To gather data on the research variables, we employed a validated questionnaire that was distributed to assistance desks/information desks of the selected institutions for self-rated replies. The datagathering period was from April to May (2023). Data were collected on-site. We used snowball sampling since the datagathering was connected to extremely subtle and individual concerns, such as mental well-being, social support, and parent–child relationships.

Furthermore, we requested assistance from the directorates of student affairs at the respective institutions in determining the target participants. We accompanied the recommendations offered by different scholars, such as those who recommended: “every item must be represented employing five samples,” that “samples of three hundred shall be regarded as appropriate,” who suggested that “the size of it ought to be twenty times bigger than the expected factors,” and who suggested that “ N  = 100–150” is adequate for conducting SEM (Anderson and Gerbing, 1988 ). Based on these scholars’ suggestions and the usual response percentage, we selected a sample size of 467 out of 355 that were found legitimate (response percentage of 76%). Male respondents comprised 55 percent, whereas female participants comprised 45 percent. Obtained surveys were utilized for research.

Measurement development

Each scale utilized in this investigation was taken and slightly modified from prior studies and had previously been authenticated by the researchers. Teti and Gelfand ( 1991 ) established the “Parent–Child Relationship” Scale, which is commonly used to measure the closeness of adolescents to their parents (Chen et al., 2015 ). It is made up of ten questions that relate to teenagers’ sentiments about their parents. Adolescents in this study were given questions like, “How openly do you talk with your parents?” The questions about perceived friend support were modified, and the sample construct was “I can count on my friends when things go wrong.”

Similarly, the study’s scale constructs of other people’s support were changed, and its example construct was “There is a special person in my life who cares about my feelings.” In this research, we assessed parental autonomy support developed by Soenens et al. ( 2007 ). It has five items: “My parents let me plan for things I want to do.” Furthermore, the assessment questions of mental well-being are measured by the five‐item scale of the World Health Organization. This scale was adapted from the study of (De Wit et al., 2007 ). Its three aspects, namely cognitive, emotional, and psychological health, were altered, and its construct was “I’ve been feeling optimistic about the future.” All of the constructs were measured on a “five-point Likert scale.” The Alpha for social support was 0.93. The Alpha value for parental autonomy support was 0.92. The Alpha for parent–child relationship was 0.90, and for mental well-being was 0.90.

Common method bias (CMB)

Since the data is collected all at once from a single source, bias concerns might surface and cast doubt on the study’s validity. The Harman single-factor test investigated the bias problem (Harman and Harman, 1976 ). The results demonstrated that each element of the suggested model could be separated into four variables, the first of which only explained 38.78% of the variation. According to this statistical value, normal biases must be lower than 50%. Therefore, our statistical data are free from prejudice.

Data analysis

We used Analysis of moment structures 25.0 to asses study hypotheses utilizing structural equation modeling (Shaffer et al., 2016 ). We used the two-step SEM technique Anderson and Gerbing ( 1988 ) recommended, beginning with CFA, to guarantee model adequacy. After that, an ultimate theoretical model was evaluated to evaluate the connections among every variable. Several fit indicators, such as 2/df, the CFI, TLI, the standardized root mean square residual (SRMR), and the root mean square error of approximation (RMSEA), were employed in the confirmatory factor analysis.

Descriptive statistics

The values for the mean, standard deviation, AVE, and Pearson’s correlations for each observed variable are displayed in Table 1 . The standard deviations ranged from 0.84 to 1.31, whereas the mean values were 1.43 to 2.94. Table 1 further reveals that the relationships between all variables analyzed are positive and substantial. Table 2 also indicates the DV of every factor for which the numerical values of average variance extracted are greater than the inter-correlational values, and the values of average variance extracted are also higher than 0.5 (Shaffer et al., 2016 ).

Measurement model

The measurement model in this work was evaluated using CFA Kline ( 2015 ), and Table 3 displays the standard factor loadings, Alpha, and CR of each component.

Social support, Parental autonomy support, Parent–child relationship, and mental well-being of students have Alpha of 0.92, 0.91, 0.90, and 0.88, respectively. These alphas exceed the suggested 0.70 threshold (Hair et al., 1998 ). The standardized factor loadings for Social support ranged from 0.78 to 0.86 for Parental autonomy support, 0.71 to 0.84 for the Parents–children relationship, 0.70 to 0.82, and 0.71 to 0.81 for the mental well-being of students. All factor loadings exceed 0.50 (Hair et al., 1998 ). The composite reliability (CR) ranges from 0.87 to 0.92 for Social support, Parental autonomy support, Parents–children relationship, and mental well-being of students, which is above the recommended value of 0.60 (Bagozzi et al., 1991 ).

In addition, we ran a serial-wise confirmatory factor analysis to ensure the model recognized different structures. The hypothesized 4-factor measurement model (Social support, Parental autonomy support, Parents–children relationship, and mental well-being of students) offered an appropriate fit to the data: χ 2  = 2693.55, Df = 946, χ 2 /df = 2.847, CFI = 0.92, TLI = 0.91, RMSEA = 0.05 and SRMR 0.04 (Table 3 ). The hypothesized 4-factor measurement model is the most suitable in each other models in Table 3 .

Table 3 shows that all observed items load the respective latent variables significantly. Other CFA models were contrasted with the proposed four-factor model. The validities are demonstrated by Table 4 ’s fit indices, providing a strong basis for evaluating the proposed four-factor model.

Hypotheses testing

We utilized a thorough structural equation modeling model with maximum likelihood estimation to analyze momentum structures and assess the study’s hypotheses. Simultaneously, hypotheses 1–2 (shown in Table 5 ) were supported by correlations (provided in Table 1 ) and SEM findings.

There is a strong positive correlation between students’ mental health and social support, as Hypothesis 1 suggests. Tables 1 and 5 provide the evidence we discovered supporting H1 (standardized β  = 0.43, t  = 7.57, p  < 0.01). According to the second hypothesis, there will be a beneficial correlation between students’ mental health and PAS. With standardized β  = 0.31, t  = 5.016, and p  < 0.01, H2 was supported.

H3 of our research uncovers that the ‘parents–children relationship significantly performs a mediating role in the association between social support and mental well-being of the students.’ Table 6 shows that when parent–child relationships are present, the β coefficient from social support and students’ mental health turns insignificant ( β  = 0.041; S.E. = 0.060; t  = 0.683; CI = −0.061, 1.012), but the indirect beta coefficient has a significant value ( β  = 0.149; S.E. = 0.063; t  = 2.365; CI = 0.337, 0.589). These findings demonstrate the mediating function of the parent–child bond in the association between students’ mental health and social support. The parent–child bond also acts as a mediator in the link between PAS and mental health, according to hypothesis 4. Table 6 shows a substantial mediating mechanism and a significant value for the beta coefficient. For H4, there is a substantial indirect correlation ( β  = 0.163; S.E. = 0.062; t  = 2.629; CI = 0.259, 0.352). Parental autonomy support and mental well-being have a direct link that eventually becomes negligible ( β  = 0.008; S.E. = 0.060; t  = 0.133; CI = −0.001, 0.013).

All formulated hypotheses of our study are accepted.

Mental health problems affect 10%–20% of students worldwide. Students’ susceptibility during the COVID-19 pandemic will likely influence this statistic. Poor mental health causes undesirable effects, including suicidal inclinations, behavioral disorders, and psychological abnormalities; hence, studies to remove or decrease the effects of bad mental health are critical. COVID-19 has made the already difficult state of youths and their mental health even more insecure. In the aftermath of a pandemic, the scale of COVID-19 the necessity for excellent research to fight MH concerns has grown exponentially. Keeping this information in mind, we developed our study subject and research questions and included parent–child relationships and linkages with students’ post-COVID-19 mental well-being of the pupils. The current research reviewed the literature on PAS, PSS, and people’s mental well-being after the COVID-19 pandemic. The literature study provided a vision of previous studies on the parent–child connection for mental well-being. According to research, the pandemic and its associated elements, such as quarantine, social isolation, and travel limitations, have been tense for students and other populations. Stress and worry caused by events such as closing schools, joblessness, poor healthcare, and uncertainty in education, job, and individual life have substantially influenced human mental and physical wellness (Pfefferbaum and North, 2020 ). We chose a paradigm that may serve the literature theoretically and practically, considering the significance of parent–child relationships after the pandemic. Prior study on the parent–child relationship has not investigated their role as a mediating variable in a unified model. The present research covers this gap in the literature by assuming that SS is positively linked with the mental well-being of students. The data analysis revealed that SS is substantially and highly positively associated with mental well-being; hence, H1 is accepted, in line with Cohen and McKay ( 2020 ), who discovered that social bonding and social support reduce psychological stress and enhance a person’s mental well-being.

Similarly, H2 investigated the association between parental autonomy support and mental well-being. It is also consistent with previous study findings that parental support has been proven to increase autonomy in young people, ultimately improving mental well-being (Inguglia et al., 2015 ). Because the conclusions indicated substantial values for each of these variables, H2 was also acceptable.

The mediation analysis was performed to determine if H3 and H4 were accepted or rejected. As previously stated in the findings section, mediating analysis was undertaken to check if the mediator increased the influence of independent variables on the dependent variable. Our research findings uncover that the ‘parents–children relationship significantly mediates the association between SS and mental well-being of the students.’ It can be viewed in the results section that the β coefficient from SS and mental well-being of the students turns insignificant in the attendance of the parent–children relationship, whereas the indirect beta coefficient has a significant value; this exhibits that the parent–children relationship plays a mediating role in the association between social support and the mental well-being of the students. Similarly, hypothesis 4 reveals that the parent–child relationship mediates the association between PAS and mental well-being. Table 6 shows that the beta value is significant, indicating a considerable mediation. The indirect association for hypothesis 4 is substantial, but the direct association between PAS and mental well-being turns insignificant. The H3 and H4 of our study are accepted.

Our findings reveal that all the proposed hypotheses were accepted, implying that SS has a good effect on the mental well-being of students and is related to a positive parent–child relationship. The findings then show that parent–child relationships positively influence mental well-being and play the role of mediating variable in the association between SS, PAS, and students’ mental well-being, which is in line with study findings that show that parent–child relationships mitigate the adverse influences of stress and foster mental wellness (Dam et al., 2023 ).

Implications

Our findings have far-reaching implications for medical practitioners, research organizations, and healthcare policymakers. Educational organizations should first become more aware of their students’ extra needs and mental health challenges. Future research should include people from various countries and ethnicities as COVID-19 control tactics and epidemic extent vary per country. Finally, the impacts of COVID-19 on students’ mental health have been overlooked. We urge instructors, higher education organizations, and mental health professionals to provide enough assistance to their students through the pandemic. Providing pupils with education to aid them in building self-efficacy, healthy parent–child relationships, and practical tools to cope with problems could help them handle the amplified stress that COVID-19 involves. It has been observed that durable and successful parent–child relationships were quite beneficial in helping pupils manage their stress. Administrators must appreciate MH practitioners’ function in supporting students seeking mental health support. Students’ capacity to tackle stress and create social support can assist them in escaping the harmful psychological impacts of the coronavirus outbreak. As a result, family, friends, and instructors should develop emotional resilience and enhance positive coping strategies among adolescents by adopting theory-tested treatments or programs. Because of constraints such as social isolation and lockdown, these treatments might be carried out in novel modes, for instance, webinars, online courses, and on-demand movies. Inter-professional probing programs and online mental behavior treatment boost students’ endurance and confidence (Schmutz, 2022 ). Furthermore, increasing social support could offer people a sense of higher psychological stability, reducing their fears and anxiety and helping them to function regularly during the pandemic. If students are urged to directly communicate their experiences and obstacles in their schooling after COVID-19, their morale will grow, and their MH will be preserved.

Limitations and future study

Our study has numerous limitations. For instance, this study relied on quantitative research; future research could use a qualitative or blended methodology to provide more intriguing outcomes. Secondly, the findings of this study were obtained by investigating eight educational institutions in Punjab province. Thirdly, because of the time limitation, we only carried out this study in one provincial unit. This research study might be broadened to other provincial units or nations in the future to generalize the study’s findings. Fourth, we obtained data from eight institutions; next, data from more institutions to be gathered to conduct the study. Finally, the current study included a mediating effect. Still, future studies may focus on using parent–child relationships as moderating variables. We suggest studying the reason for integrating PSS into the cognitive vulnerability model. As a result, new concerns have developed regarding the viability and significance of progressing to an integrative model, etiological paradigms, and innovative prospects for study and practical implementations.

The COVID-19 pandemic and the complex reaction techniques to halt its spread harm psychological and emotional well-being. Students’ lives have been damaged by social alienation and self-isolation. These effects must be detected, analyzed, and dealt with to guarantee the well-being of people like students. As a result, the present research sought to examine the influence of parental-child relationships, PAS, and SS in enhancing students’ mental well-being by gathering data from post-COVID-19. Students enrolling in Pakistani universities provided data. A survey for the survey was created to collect information from 355 students. SPSS was used to compute descriptive statistics, whereas AMOS structural equation modeling was employed to test hypotheses. These findings underlined the importance of the parent–child connection in dealing with complicated unfavorable conditions since it influences their mental results, particularly their psychological health. Optimistic and adverse relationships are opposed. Students who utilized primarily constructive relationship mechanisms with their parents experienced less emotional distress than those who employed more detrimental connection mechanisms with their parents (Budimir et al., 2021 ). Furthermore, the research emphasized the need for social support, such as friends and family, as well as parental autonomy support, in the fight against mental disorders. The findings also revealed that students require not only family support but also help from friends and others to create good relationships with parents to deal with psychological difficulties and stress produced by numerous sources.

Data availability

According to the confidential agreements with the participants, the dataset analyzed during the current study is not publicly available. The raw data supporting the conclusions of this article will be made available by the corresponding author upon reasonable request.

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This research is supported by the Jiangsu Funding Program for Excellent Postdoctoral Talent (No. 2022ZB643).

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Arif Jameel, Zhiqiang Ma, Mingxing Li, Abid Hussain, Muhammad Asif & Yan Wang

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Jameel, A., Ma, Z., Li, M. et al. The effects of social support and parental autonomy support on the mental well-being of university students: the mediating role of a parent–child relationship. Humanit Soc Sci Commun 11 , 622 (2024). https://doi.org/10.1057/s41599-024-03088-0

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The Power of Parks to Strengthen Community: A Special Report

Primary Author: Lisa W Foderaro , Senior Writer and Researcher, Trust for Public Land

Published May, 2024

High-quality park systems possess the transformative power to help mend America’s fraying social fabric.

• Executive Summary

Americans’ sense of community is being sorely tested. Studies show that people are experiencing extreme levels of loneliness, polarization, and division. Places that historically brought people of different backgrounds together have faded in importance or have themselves become battlegrounds, from houses of worship to social clubs to school boards of education. Parks remain a neutral public gathering place where community members can meet, collaborate, and become empowered. Park leaders can foster those connections through a variety of approaches, programs, and partnerships.

Dakai Brown of Bridgeport, Connecticut, like children around the country, benefits tremendously from communities where social connections are strong. High-quality parks are a contributing factor to social capital, which can deliver lower mortality, reduced depression, and increased economic mobility.

The Findings: Parks can strengthen communities and help bridge divides

Recommendations: Parks departments can deploy strategies to build community

It is no longer enough for cities to create high-quality green spaces. To create strong connections with the community and between residents, parks departments must activate those spaces with culturally representative programs. Photo: Andy Richter

Sherry Taylor, a participant in Raleigh’s On Common Ground program, attended an outdoor viewing party for the Women’s Soccer World Cup at the city’s Moore Square and met several neighbors, including one who has since become a good friend. Photo: Paul Atkinson

THE PROBLEM: America’s Declining Social Capital

“My daughter fell in love with women’s soccer that night,” Taylor recalls. And Taylor, herself, forged a new and critical friendship with a woman she might never have met otherwise. Also a single mother, the other woman and Sherry have grown close and now regularly carpool and help each other with childcare.

“I’m able to pick her daughter up if she can’t, whether because of work or a medical appointment, and vice versa,” Taylor explained. “It’s hard being a single parent, and it’s just good to have someone else to lean on and bond with.”

This kind of neighborly camaraderie is known as social capital , “the ways in which our lives are made more productive by social ties.” Many studies, including Social Capital and Health and Social Capital and Economic Development: A Neighborhood Perspective , show that it’s associated with several significant community-wide benefits, including improved health, social resilience, civic participation, environmental volunteering, and economic well-being. Communities with more social connections see numerous positive outcomes, including lower mortality, reduced depression, and increased economic mobility, and are more resilient to disasters.

But studies also show that social capital has declined in recent decades and that Americans confront polarization along political , economic , ideological , racial and ethnic lines.

THE HYPOTHESIS: Parks Can Help Community Cohesion

At a time when Americans’ trust in many public institutions has sunk to disturbing levels, parkgoers report more positive attitudes.

For example, almost two-thirds of American adults express little to no confidence in the future of our political system. By contrast, people who live near parks are more likely to be satisfied with their local government .

In the Assembly Civic Engagement Survey of over 5,000 people in 26 U.S. communities, those living near popular public parks reported 29 percent greater satisfaction with their parks and recreation departments, 14 percent greater satisfaction with their police, and 13 percent greater satisfaction with their mayor, compared with to people not living near parks—an effect that was even stronger when parks were well-maintained and easily accessible. The findings were controlled for age, Hispanic origin, number of children, political party affiliation, health status, and other factors.

So in 2024 TPL endeavored to explore and expand the existing body of evidence that suggests a link between high-quality park systems and measures of social connectivity.

Parks departments are increasingly creating programming within their green spaces to attract community participation. Free or low-cost exercise programs, like the one at Denver’s Mestizo-Curtis Park, are a great way to get community members moving while fostering social connection.

Students at The Pacific School in Brooklyn, New York, explore the new plants in the renovated Community Schoolyard® garden during the ribbon cutting ceremony in June 2023. These spaces are not just for students. The schoolyards are open to the public after school hours and on weekends to ensure that neighbors have close-to-home access to high-quality park space. Photo: Alexa Hoyer

AN EXPLORATION: Methodology

First, TPL’s annual ParkScore® index ranks parks systems in the 100 most populous U.S. cities based on five factors: access, equity, acreage, investment, and amenities. Additional information on the methodology can be found at tpl.org/parkscore/about .

Second, this year TPL surveyed all public and private organizations managing publicly accessible parks across the 100 most populous U.S. cities to learn how they’re activating their systems to bridge divides between groups that, at best, have little contact with each other and, at worst, experience tension or hostility. A total of 868 examples were submitted from 208 different organizations. Additional details on this survey of park agencies, including methodology and additional findings, can be found here .

Third, data scientists at TPL’s Land and People Lab investigated the association between cities’ ParkScore index rankings and their social capital, as measured by two indicators from the Social Capital Atlas : economic connectedness and rates of volunteering. The researchers behind the Social Capital Atlas developed these indicators by analyzing billions of Facebook relationships for the 72 million U.S. adults aged 25–44 who use Facebook (84% of all U.S. adults aged 25–44). Economic connectedness, or “cross-group” relationships, measures the percentage of friendships between low- and high-income individuals in a given geography, while the volunteering rate measures the percentage of people who are affiliated with a volunteering group. The statistical analysis evaluated the association between a city’s park system quality and social capital while controlling for other factors such as race/ethnicity, urbanicity, transiency, poverty, education, and family structure.

Fourth, with support from the Walmart Foundation, TPL collaborated with nine smaller cities to field-test several tactics for activating park systems and programs in order to strengthen social capital. The Walmart Foundation’s Caring and Connected Communities grant program focuses on bringing people together for meaningful encounters and giving them the skills to build trust and empathy.

“Parks and public spaces are part of that focus,” said Melissa Rhodes Carter, the Walmart Foundation’s senior manager for Community Resilience. “They can play an important role in providing opportunities for people to form relationships that have the potential to help us all live better.”

A Caring and Connected Communities grant helped TPL launch its On Common Ground program in 2023. That program began with the release of “ The Common Ground Framework: Building Community Power Through Park and Green Space Engagement, ” a paper that presented a theoretical three-part model for building community relationships, community identity, and community power.

Following the paper, TPL launched an intensive effort to engage park agencies in nine communities across the United States to test methods of uniting residents of different socioeconomic, racial, ethnic, and generational backgrounds.

“We want to learn from the projects and then share data, tools, and best practices with a wide swath of practitioners,” said Cary Simmons, director of Community Strategies at TPL, who is leading the On Common Ground program. “Essentially, we’re field-testing activities that we hypothesize will have broad applications.” Trust for Public Land is currently evaluating and plans to publish the results of each city’s efforts, but early case studies from several communities are included in the next section.

Mariano Rodriguez (left) and Collin McSpirit stroll on the boardwalk in Bridgeport, Connecticut’s Seaside Park. At a time when Americans express feelings of loneliness, isolation, and disconnection from neighbors, shared public spaces are important in helping people form and maintain relationships. Photo: Kristyn Miller

THE RESULTS: Key Strategies & Case Studies

Table 1 Data source: TPL analysis comparing two measures of social capital from the Social Capital Atlas (https://socialcapital.org/), economic connectedness (‘cross-group’ relationships) and volunteering rate, with TPL’s measure of park system quality—the ParkScore Index.

In the top 25 ParkScore cities, for example, there were, on average, 26 percent more social connections between low- and high-income individuals (“cross-group” relationships) than in lower-ranked cities.

Also in the top 25 ParkScore cities, people were 60 percent more likely to volunteer than those living in lower-ranked cities.

These patterns held after controlling for race/ethnicity, education, poverty, urbanicity, family structure, and transiency.

In layering those findings with TPL’s 2024 survey responses and with reports from the nine On Common Ground communities, four key strategies for building social connections emerged. They are:

Each of these strategies is discussed in the subsections that follow, accompanied by illustrative case studies from ParkScore and On Common Ground cities.

Dr. Hahrie Han, a professor of political science at Johns Hopkins University who studies democracy and civic engagement, said that over the past half-century, American society has seen a withering of the sorts of common spaces that allow people to engage with one another.

“The audacious, beautiful, and exciting promise at the heart of democracy is the idea that putting people into community with each other creates opportunities to learn the capacities, skills, and motivations needed to forge a common life together,” she said. “So it’s ever more important that we have places like parks where people naturally encounter other people. It’s through those encounters that common interests can emerge that are grounded in, but also transcend, self-interest. I see things like parks as an integral part of our civic infrastructure. When we don’t have a multiplicity of those spaces, we know that people can revert back to more parochial tendencies that can create the kind of divisiveness we see in some parts of our society.”

This idea—that parks are an integral part of our civic infrastructure—is more than an accidental by-product. In the ParkScore survey, nearly all cities (91 of 100) reported they’re actively developing civic infrastructure, such as hosting voter registration drives and polling sites in their park systems (Table 2). Other examples include training community members in civic engagement, allowing public protests at park facilities, and welcoming community organizers. These efforts engage people as citizens and thereby revive the democratic ideal of the public square.

Table 2 Data source: 2024 TPL City Park Facts Survey of all public and private park organizations across the 100 most populous cities. The counts reflect the number of cities with at least one organization reporting the given activity.

The 15 advisory council members reflect the city’s rich racial and ethnic diversity. Using some of their On Common Ground funds, BREC compensated committee members with $1,000 stipends, hired a consultant to lead conversations, and paired up the council members to plan community engagement events.

“It was really important for us to introduce the council to [the issue of racism] as soon as possible,” said Andrea Roberts, BREC’s chief operating officer. “We have an unfortunate history here in Baton Rouge, where segregation was a thing even after the federal government said it was illegal. This continued in our community for years. So people are hurting from that and there’s resentment. Those things need to be talked about.”

The work of the community advisory council, which met every month for a year, included a series of community events, each hosted by two council members from different geographic areas and different racial or ethnic backgrounds. BREC provided a budget and guidance, but the teams were given creative freedom. Events ranged from a party for children to make mini-Mardi Gras floats to a Martin Luther King Jr. Day celebration in a local park.

In most ParkScore cities, park agencies’ primary community engagement tools are public meetings. This can result in a top-down approach in which the community is only superficially engaged.

Some parks departments employ the International Association for Public Participation’s (IAP2) “ Spectrum of Public Participation ” model, which features a continuum of citizen engagement: inform, consult, involve, collaborate, empower. In the 2024 TPL survey of the nation’s 100 largest cities, public park agencies were nearly five times more likely to inform the public (“always” or “most of the time”) than to empower the public (Table 3). Only 18 percent of responding cities said they empower the local residents by placing final decision-making in the hands of the public.

Table 3: When planning park projects, systems, and recreation programs, how often does your agency inform, consult, involve, collaborate, and/or empower local residents? Data source: 2024 TPL City Park Facts Survey of all public and private park organizations in the 100 most populous cities. The percentages in this table reflect only each city’s primary park and recreation agency. Sample size = 81 unique cities.

According to TPL data, these trends are on the upswing as park officials actively engage resident collaborators. TPL’s research suggests that cities that shift their efforts toward the right end of the spectrum are more likely to move the needle on measures of social capital.

Across the country, about two-thirds of ParkScore cities are investing in community engagement, either paying community members for their input or hiring full-time community engagement staff (55). Park leaders are hiring and training community organizers and activators to pro- gram their green spaces as hubs for community connection.

“If you show up and say, ‘Here is your brick box of a rec center,’ for example, and the community says they hate it, you won’t get any buy-in to whatever it is you’re building,” he explains. “If you include the community from the outset, you build in participation, volunteerism, advocacy, and stewardship.”

Baltimore City Recreation and Parks now includes the community at every stage. It holds periodic “participatory urbanism” forums, giving residents an opportunity to weigh in on big-picture issues like policy and capital programs. It also invites community members to charrettes—design workshops with park professionals and landscape architects to brainstorm ideas for new green spaces or park renovations. Increasingly, Baltimore is asking children what they think, too.

“When we did playground designs, there was a roomful of adults,” Almaguer says of past projects. “But what do the kids want?” For two new playgrounds at Leon Day Park and Alhambra Park, the department held charrettes at a local rec center for 5- to 12-year-olds. The children designed and built their own playgrounds using popsicle sticks, pipe cleaners, marshmallows, and gumdrops. “We gave them dot stickers to place on things they wanted, like swings and tall towers for climbing,” he added, “and they also voted.”

Baltimore is also taking steps to strengthen its network of park friends groups—and to nurture new ones. The parks department invites 30 such groups to quarterly “Grow” workshops. Each meeting has a different theme—permits, say, or maintenance. “Even though we have a topic, we leave it open for cross-pollination between the groups,” Almaguer added. “So they might say, ‘Hey, what do you do about insurance?’ or ‘How do you write a grant or run an event?’ It’s an opportunity for peer-to-peer conversations.”

Since 2019, the department has also helped three under-resourced neighborhoods start new friends groups. “We all cherish and love parks,” he said. “It is your place of social capital; it’s your respite; it’s your oasis.

TPL has sought to elevate community in all its projects, whether large-scale land protections or small city parks. Community engagement—in public meetings, at farmers markets, in classrooms, and elsewhere—begins long before a formal park design is created or a shovel goes in the ground. It often endures—to propel programming and stewardship—after officials snip the ribbon on opening day. Increasingly, TPL is identifying resident experts in the communities where it works, compensating them for conducting surveys, assisting in workshops, or serving as a liaison with fellow residents.

A little more than half of ParkScore cities (51) are responding to division and polarization by activating their parks—creating programs or facilitating events—with a heavy focus on bringing unlikely groups together, through a series of creative and culturally specific programs that engage new audiences and park user groups. The data suggest that creating high-quality, accessible parks isn’t enough. If cities want to improve and grow social cohesion, they need to create opportunities for residents to come together, and those opportunities should reflect the cultures, interests, and priorities unique to that community.

Tulsa’s weekly Global Gatherings, which drew nearly 4,500 people, highlighted traditions from around the country and the world, including Native American dress and dancing in order to immerse community members in the many rich cultures present throughout the city. Photo: Courtesy of Gathering Place

One of the 100 cities evaluated by TPL’s ParkScore® index, Tulsa developed its five-year-old Gathering Place park with the goal of uniting Tulsans of all backgrounds. The 66-acre park, which had a $465 million price tag, may well be the most expensive local park in U.S. history. (About $200 million of the cost was covered by the George Kaiser Family Foundation, while the city, as well as other foundations and businesses, picked up the rest.)

All programs are free. Chief among them is Global Gatherings, a 13-week program that last year featured weekly events highlighting traditions from more than a dozen regions of the world—Southeast Asia, Africa, the Caribbean, Native America, and others. The program drew nearly 4,500 people and involved more than 80 community partners in activities that featured food, music, dance, traditional attire, storytelling, and crafts.

“Global Gatherings is a collaborative celebration of cultures, and we were ecstatic with the community’s response to the multi-week program,” said Julio Badin, executive director of Gathering Place. “Tulsans of all global backgrounds gathered at the park to share and highlight their traditions to immerse other community members in their rich cultures through art, storytelling, food, music, and dance.”

Jacque Riggs, a jewelry designer and silversmith, arrived in Tulsa from Peru in 2002. Last summer, Riggs and her daughter, Zadith Rodriguez, a 28-year-old printmaker, participated in Global Gatherings. Riggs, 50, helped children make bracelets from brightly colored huayruro seeds. Such bracelets are considered protective amulets, given to newborns to ward off evil spirits. Zadith, for her part, showed children how to make prints of llamas and mountain scenes using paper and inks. There was even a map of Peru.

“It’s always nice to be able to talk a bit about your culture,” Riggs said. “The children were excited and seemed really interested in what we were doing. I also took some bracelets to people at the festival who were from Brazil and Venezuela and said, ‘Hey, I want to give this to you for good luck.’” Building Partnerships In 2023, TPL celebrated its 50th anniversary, and CEO Diane Regas reflected on the organization’s earliest days and earliest partnerships in the San Francisco Bay Area. The organization’s founders, she noted, partnered with the Black Panthers to reclaim vacant lots to create dozens of miniparks throughout the city. “Those early efforts in Oakland were more than just creating parks and green spaces,” she said. “They were about building community, fostering collaboration, and shaping a legacy of hope that continues to influence our work to this day.”

Park leaders across the country report employing partnerships with organizations that deliver park stewardship (74), arts and culture (61), public health (55), and literacy (45) programming. But partnerships that leverage green spaces to address underlying community- level challenges like housing stability (15) and immigrant inclusion (22) are relatively scant (Table 4).

Trust for Public Land examined the connection between a city’s ParkScore ranking and two key indicators of social capital, including volunteerism rates . Parks systems that create and facilitate opportunities for residents to volunteer in their communities can contribute to greater cohesion among neighbors . Photo: Justin Bartels

Table 4 Data source: 2024 TPL City Park Facts Survey of all public and private park organizations across the 100 most populous cities. The counts reflect the number of cities with at least one organization reporting the given activity.

Trust for Public Land research indicates that parks departments should engage residents at every phase of park development, from design to programming. Such was the case at San Geronimo Commons, a former golf course turned community green space in California. Photo: Kevin Quach

CONCLUSION: Community on the Cusp — What Can Be Done

Parks hold tremendous potential for repairing our frayed social fabric, making it incumbent upon the entire parks and recreation field, as well as nonprofit and philanthropic organizations, government agencies, the private sector, and private citizens, to lean into funding and programming opportunities.

The following strategies can optimize the power of parks to elevate social connectedness.

At the local level: Prioritize community engagement. This must be a priority, included in master plans and set forth in local park policy. The process of reaching out to residents and cultivating relationships takes time and intention, so local governments need to dedicate adequate resources in the form of staff, money, and training.

Parks departments should engage residents at every phase of park development, from the spark of an idea for a new park to the creation of park-based programming. This can happen through a one-time design workshop, for example, or through an advisory council or planning committee that meets for months or years. Park professionals should also make full use of friends groups and help establish new ones in under-resourced neighborhoods. And they should consider compensating resident liaisons.

The TPL survey found evidence that there’s a strong public will for funding these efforts. Parks budgets were up across the board in the past year. Total public and private spending on parks and recreation in the 100 most populous cities climbed to $11.2 billion, up from $9.7 billion in 2022 and $8.7 billion five years ago. In fact, after adjusting for inflation, it was the most spent annually on local parks in the largest U.S. cities since before the Great Recession in 2007. This year’s record high was primarily driven by increased capital funding for park renovations and acquisitions from a one-time infusion of federal funds via COVID-19 stimulus money and the Inflation Reduction Act. More cities are investing in their park systems, too, as an outcome of the pent-up demand for green space witnessed during the pandemic.  Private philanthropies, such as the Walmart Foundation and others, have also stepped in and should continue to help towns and cities heal divides and strengthen community.

At the federal level: Park advocates, administrators, and users can support the Outdoors for All Act (O4A). Among other things, O4A would improve the Outdoor Recreation

Legacy Partnership (ORLP), a grant program run by the National Park Service that provides funding for communities that need parks the most.  Specifically, the legislation, which TPL is championing in Congress, would make ORLP permanent, avoiding the need to renew the program annually. If passed, the bill would also allow federally recognized tribes to apply to the funding program for the first time, while letting tribes, cities, and nonprofits apply directly to the National Park Service (instead of having to go through their state governments). Finally, the legislation would lower the population requirement to 25,000, meaning smaller cities and towns would be eligible for funding.

Trust for Public Land is working in close collaboration with several partners, city officials, and community groups on a new park on a former brownfield in San Francisco. When completed, the 10-acre India Basin Waterfront Park , in the Bayview-Hunters Point neighborhood, will offer boat access, as well as walking and bike paths, an extensive picnic area, basketball courts, playgrounds, restrooms, a food pavilion, a history museum and community center, lawns, piers, and a floating dock. The park also features restoration of wetlands, expansion of wildlife habitat, and naturalization of the waterfront edge. The park was designed with extensive community input and will protect residents from sea level rise, as well as offer needed resources.

The park is emerging in an area that, from the 1850s to the 1900s, thrived as its shoreline was used as a civilian and military shipyard and slaughterhouse. Those industries, now defunct, left significant environmental damage, however, impacting the land and cutting off residents from the waterfront. The neighborhood, which is one of the few remaining historically Black neighborhoods in San Francisco, has also suffered from deep neglect of basic infrastructure and generations of social injustice.

The goal in developing the new park is as much to create a spectacular new green space as it is to strengthen the neighborhood—economically, socially, and politically. To achieve that, an Equitable Development Plan informs every aspect of the project and was developed by the community and the four project partners: TPL, the San Francisco Parks Alliance, the San Francisco Recreation and Parks Department, and the A. Philip Randolph Institute. Local residents receive training in construction jobs through a robust workforce development program. And a tech hub within the park is now providing free Wi-Fi, loaner laptops, and tech support, even before the park’s official opening.

Get The Power of Parks to Strengthen Community

Contributors Tim Almaguer , Baltimore City Department of Recreation and Parks, Community Engagement and Strategic Partnership Division | Julio Badin , Gathering Place | Melissa Rhodes Carter , Walmart Foundation | Howard Frumkin | Dr Hahrie Han , Johns Hopkins University | Will Klein | Hannah Kohut | Keith Maley | Kevin Niu |  Cary Simmons | Sherry Taylor | Diane Regas | Jacque Riggs | Andrea Roberts , East Baton Rouge Parish Parks and Recreation Department (BREC) | Levar Robinson , BREC Community Advisory Council | Zadith Rodriguez | Geneva Vest | Šárka Volejníková | Dan Walsh | Deborah Williams

• The Problem
• An Exploration
• The Results
• Study: Baton Rouge
• Study: Baltimore
• Study: Tulsa
• Study: San Francisco
• Back to Top