example of cause and effect hypothesis

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Causal research: definition, examples and how to use it.

16 min read Causal research enables market researchers to predict hypothetical occurrences & outcomes while improving existing strategies. Discover how this research can decrease employee retention & increase customer success for your business.

What is causal research?

Causal research, also known as explanatory research or causal-comparative research, identifies the extent and nature of cause-and-effect relationships between two or more variables.

It’s often used by companies to determine the impact of changes in products, features, or services process on critical company metrics. Some examples:

• How does rebranding of a product influence intent to purchase?
• How would expansion to a new market segment affect projected sales?
• What would be the impact of a price increase or decrease on customer loyalty?

To maintain the accuracy of causal research, ‘confounding variables’ or influences — e.g. those that could distort the results — are controlled. This is done either by keeping them constant in the creation of data, or by using statistical methods. These variables are identified before the start of the research experiment.

As well as the above, research teams will outline several other variables and principles in causal research:

• Independent variables

The variables that may cause direct changes in another variable. For example, the effect of truancy on a student’s grade point average. The independent variable is therefore class attendance.

• Control variables

These are the components that remain unchanged during the experiment so researchers can better understand what conditions create a cause-and-effect relationship.  

This describes the cause-and-effect relationship. When researchers find causation (or the cause), they’ve conducted all the processes necessary to prove it exists.

• Correlation

Any relationship between two variables in the experiment. It’s important to note that correlation doesn’t automatically mean causation. Researchers will typically establish correlation before proving cause-and-effect.

• Experimental design

Researchers use experimental design to define the parameters of the experiment — e.g. categorizing participants into different groups.

• Dependent variables

These are measurable variables that may change or are influenced by the independent variable. For example, in an experiment about whether or not terrain influences running speed, your dependent variable is the terrain.  

Why is causal research useful?

It’s useful because it enables market researchers to predict hypothetical occurrences and outcomes while improving existing strategies. This allows businesses to create plans that benefit the company. It’s also a great research method because researchers can immediately see how variables affect each other and under what circumstances.

Also, once the first experiment has been completed, researchers can use the learnings from the analysis to repeat the experiment or apply the findings to other scenarios. Because of this, it’s widely used to help understand the impact of changes in internal or commercial strategy to the business bottom line.

Some examples include:

• Understanding how overall training levels are improved by introducing new courses
• Examining which variations in wording make potential customers more interested in buying a product
• Testing a market’s response to a brand-new line of products and/or services

So, how does causal research compare and differ from other research types?

Well, there are a few research types that are used to find answers to some of the examples above:

1. Exploratory research

As its name suggests, exploratory research involves assessing a situation (or situations) where the problem isn’t clear. Through this approach, researchers can test different avenues and ideas to establish facts and gain a better understanding.

Researchers can also use it to first navigate a topic and identify which variables are important. Because no area is off-limits, the research is flexible and adapts to the investigations as it progresses.

Finally, this approach is unstructured and often involves gathering qualitative data, giving the researcher freedom to progress the research according to their thoughts and assessment. However, this may make results susceptible to researcher bias and may limit the extent to which a topic is explored.

2. Descriptive research

Descriptive research is all about describing the characteristics of the population, phenomenon or scenario studied. It focuses more on the “what” of the research subject than the “why”.

For example, a clothing brand wants to understand the fashion purchasing trends amongst buyers in California — so they conduct a demographic survey of the region, gather population data and then run descriptive research. The study will help them to uncover purchasing patterns amongst fashion buyers in California, but not necessarily why those patterns exist.

As the research happens in a natural setting, variables can cross-contaminate other variables, making it harder to isolate cause and effect relationships. Therefore, further research will be required if more causal information is needed.

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How is causal research different from the other two methods above?

Well, causal research looks at what variables are involved in a problem and ‘why’ they act a certain way. As the experiment takes place in a controlled setting (thanks to controlled variables) it’s easier to identify cause-and-effect amongst variables.

Furthermore, researchers can carry out causal research at any stage in the process, though it’s usually carried out in the later stages once more is known about a particular topic or situation.

Finally, compared to the other two methods, causal research is more structured, and researchers can combine it with exploratory and descriptive research to assist with research goals.

Summary of three research types

What are the advantages of causal research?

• Improve experiences

By understanding which variables have positive impacts on target variables (like sales revenue or customer loyalty), businesses can improve their processes, return on investment, and the experiences they offer customers and employees.

• Help companies improve internally

By conducting causal research, management can make informed decisions about improving their employee experience and internal operations. For example, understanding which variables led to an increase in staff turnover.

• Repeat experiments to enhance reliability and accuracy of results

When variables are identified, researchers can replicate cause-and-effect with ease, providing them with reliable data and results to draw insights from.

• Test out new theories or ideas

If causal research is able to pinpoint the exact outcome of mixing together different variables, research teams have the ability to test out ideas in the same way to create viable proof of concepts.

• Fix issues quickly

Once an undesirable effect’s cause is identified, researchers and management can take action to reduce the impact of it or remove it entirely, resulting in better outcomes.

What are the disadvantages of causal research?

• Provides information to competitors

If you plan to publish your research, it provides information about your plans to your competitors. For example, they might use your research outcomes to identify what you are up to and enter the market before you.

• Difficult to administer

Causal research is often difficult to administer because it’s not possible to control the effects of extraneous variables.

• Time and money constraints

Budgetary and time constraints can make this type of research expensive to conduct and repeat. Also, if an initial attempt doesn’t provide a cause and effect relationship, the ROI is wasted and could impact the appetite for future repeat experiments.

• Requires additional research to ensure validity

You can’t rely on just the outcomes of causal research as it’s inaccurate. It’s best to conduct other types of research alongside it to confirm its output.

• Trouble establishing cause and effect

Researchers might identify that two variables are connected, but struggle to determine which is the cause and which variable is the effect.

• Risk of contamination

There’s always the risk that people outside your market or area of study could affect the results of your research. For example, if you’re conducting a retail store study, shoppers outside your ‘test parameters’ shop at your store and skew the results.

How can you use causal research effectively?

To better highlight how you can use causal research across functions or markets, here are a few examples:

Market and advertising research

A company might want to know if their new advertising campaign or marketing campaign is having a positive impact. So, their research team can carry out a causal research project to see which variables cause a positive or negative effect on the campaign.

For example, a cold-weather apparel company in a winter ski-resort town may see an increase in sales generated after a targeted campaign to skiers. To see if one caused the other, the research team could set up a duplicate experiment to see if the same campaign would generate sales from non-skiers. If the results reduce or change, then it’s likely that the campaign had a direct effect on skiers to encourage them to purchase products.

Improving customer experiences and loyalty levels

Customers enjoy shopping with brands that align with their own values, and they’re more likely to buy and present the brand positively to other potential shoppers as a result. So, it’s in your best interest to deliver great experiences and retain your customers.

For example, the Harvard Business Review found that an increase in customer retention rates by 5% increased profits by 25% to 95%. But let’s say you want to increase your own, how can you identify which variables contribute to it?Using causal research, you can test hypotheses about which processes, strategies or changes influence customer retention. For example, is it the streamlined checkout? What about the personalized product suggestions? Or maybe it was a new solution that solved their problem? Causal research will help you find out.

Improving problematic employee turnover rates

If your company has a high attrition rate, causal research can help you narrow down the variables or reasons which have the greatest impact on people leaving. This allows you to prioritize your efforts on tackling the issues in the right order, for the best positive outcomes.

For example, through causal research, you might find that employee dissatisfaction due to a lack of communication and transparency from upper management leads to poor morale, which in turn influences employee retention.

To rectify the problem, you could implement a routine feedback loop or session that enables your people to talk to your company’s C-level executives so that they feel heard and understood.

How to conduct causal research first steps to getting started are:

1. Define the purpose of your research

What questions do you have? What do you expect to come out of your research? Think about which variables you need to test out the theory.

2. Pick a random sampling if participants are needed

Using a technology solution to support your sampling, like a database, can help you define who you want your target audience to be, and how random or representative they should be.

3. Set up the controlled experiment

Once you’ve defined which variables you’d like to measure to see if they interact, think about how best to set up the experiment. This could be in-person or in-house via interviews, or it could be done remotely using online surveys.

4. Carry out the experiment

Make sure to keep all irrelevant variables the same, and only change the causal variable (the one that causes the effect) to gather the correct data. Depending on your method, you could be collecting qualitative or quantitative data, so make sure you note your findings across each regularly.

5. Analyze your findings

Either manually or using technology, analyze your data to see if any trends, patterns or correlations emerge. By looking at the data, you’ll be able to see what changes you might need to do next time, or if there are questions that require further research.

6. Verify your findings

Your first attempt gives you the baseline figures to compare the new results to. You can then run another experiment to verify your findings.

7. Do follow-up or supplemental research

You can supplement your original findings by carrying out research that goes deeper into causes or explores the topic in more detail. One of the best ways to do this is to use a survey. See ‘Use surveys to help your experiment’.

Identifying causal relationships between variables

To verify if a causal relationship exists, you have to satisfy the following criteria:

• Nonspurious association

A clear correlation exists between one cause and the effect. In other words, no ‘third’ that relates to both (cause and effect) should exist.

• Temporal sequence

The cause occurs before the effect. For example, increased ad spend on product marketing would contribute to higher product sales.

• Concomitant variation

The variation between the two variables is systematic. For example, if a company doesn’t change its IT policies and technology stack, then changes in employee productivity were not caused by IT policies or technology.

How surveys help your causal research experiments?

There are some surveys that are perfect for assisting researchers with understanding cause and effect. These include:

• Employee Satisfaction Survey – An introductory employee satisfaction survey that provides you with an overview of your current employee experience.
• Manager Feedback Survey – An introductory manager feedback survey geared toward improving your skills as a leader with valuable feedback from your team.
• Net Promoter Score (NPS) Survey – Measure customer loyalty and understand how your customers feel about your product or service using one of the world’s best-recognized metrics.
• Employee Engagement Survey – An entry-level employee engagement survey that provides you with an overview of your current employee experience.
• Customer Satisfaction Survey – Evaluate how satisfied your customers are with your company, including the products and services you provide and how they are treated when they buy from you.
• Employee Exit Interview Survey – Understand why your employees are leaving and how they’ll speak about your company once they’re gone.
• Product Research Survey – Evaluate your consumers’ reaction to a new product or product feature across every stage of the product development journey.
• Brand Awareness Survey – Track the level of brand awareness in your target market, including current and potential future customers.
• Online Purchase Feedback Survey – Find out how well your online shopping experience performs against customer needs and expectations.

That covers the fundamentals of causal research and should give you a foundation for ongoing studies to assess opportunities, problems, and risks across your market, product, customer, and employee segments.

If you want to transform your research, empower your teams and get insights on tap to get ahead of the competition, maybe it’s time to leverage Qualtrics CoreXM.

Qualtrics CoreXM provides a single platform for data collection and analysis across every part of your business — from customer feedback to product concept testing. What’s more, you can integrate it with your existing tools and services thanks to a flexible API.

Qualtrics CoreXM offers you as much or as little power and complexity as you need, so whether you’re running simple surveys or more advanced forms of research, it can deliver every time.

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The Craft of Writing a Strong Hypothesis

Table of Contents

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good  alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

• Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
• Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher  the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

• A research hypothesis has to be simple yet clear to look justifiable enough.
• It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
• It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
• A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
• If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
• A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

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Examples of Simple Experiments in Scientific Research

Sam Edwards / Getty Images

A simple experiment is used by researchers to determine if changes in one variable lead to changes in another variable — in other words, to establish cause-and-effect. 

For example, in a simple experiment looking at the effectiveness of a new medication, study participants would be  randomly assigned to one of two groups: one of these would be the control group and receive no treatment, while the other group would be the experimental group that receives the treatment being studied.

The Elements of a Simple Experiment

A simple experiment is composed of several key elements:

• The Experimental Hypothesis: This is a statement that predicts the treatment will cause an effect. It will always be phrased as a cause-and-effect statement. For example, researchers might phrase a hypothesis as: "Administration of Medicine A will result in a reduction of symptoms of Disease B."
• The Null Hypothesis: This is a hypothesis stating the experimental treatment will have no effect on the participants or dependent variables. It's important to note that failing to find an effect of the treatment does not mean there is no effect. The treatment might impact another variable the researchers are not measuring in the current experiment.
• The Independent Variable :  The treatment variable being manipulated by the experimenter.
• The Dependent Variable : The response the researchers are measuring.
• The Control Group:  The group of randomly assigned individuals who do not receive the treatment. The measurements taken from the control group will be compared to those in the experimental group to determine if the treatment had an effect.
• The Experimental Group:  This group of randomly assigned individuals who will receive the treatment being tested. 

Determining the Results of a Simple Experiment

Once the data from the simple experiment is gathered, researchers compare the results of the experimental group to those of the control group to determine if the treatment had an effect. Due to the omnipresent possibility of errors, it's not possible to be 100 percent sure of the relationship between two variables. There can always be be unknown variables influencing the outcome of the experiment.

Despite this challenge, there are ways to determine if there most likely is a meaningful relationship between the variables. To do this, scientists use inferential statistics—a branch of science that deals with drawing inferences about a population based on measurements taken from a representative sample of that population.

The key to determining if a treatment had an effect is to measure the statistical significance. Statistical significance shows that the relationship between the variables is probably not due to mere chance and that a real relationship most likely exists between the two variables.

Statistical significance is often represented like this:

p < 0.05

A p-value of less than .05 indicates that the results likely are due to chance and that the probability of obtaining these results would be less than 5%.

There are a number of different means of measuring statistical significance. The one used will depend on the type of research design that was used for the experiment.

Skelly AC. Probability, proof, and clinical significance .  Evid Based Spine Care J . 2011;2(4):9-11. doi:10.1055/s-0031-1274751

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

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• 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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Causal Research: Definition, Design, Tips, Examples

Appinio Research · 21.02.2024 · 34min read

Ever wondered why certain events lead to specific outcomes? Understanding causality—the relationship between cause and effect—is crucial for unraveling the mysteries of the world around us. In this guide on causal research, we delve into the methods, techniques, and principles behind identifying and establishing cause-and-effect relationships between variables. Whether you're a seasoned researcher or new to the field, this guide will equip you with the knowledge and tools to conduct rigorous causal research and draw meaningful conclusions that can inform decision-making and drive positive change.

What is Causal Research?

Causal research is a methodological approach used in scientific inquiry to investigate cause-and-effect relationships between variables. Unlike correlational or descriptive research, which merely examine associations or describe phenomena, causal research aims to determine whether changes in one variable cause changes in another variable.

Importance of Causal Research

Understanding the importance of causal research is crucial for appreciating its role in advancing knowledge and informing decision-making across various fields. Here are key reasons why causal research is significant:

• Establishing Causality:  Causal research enables researchers to determine whether changes in one variable directly cause changes in another variable. This helps identify effective interventions, predict outcomes, and inform evidence-based practices.
• Guiding Policy and Practice:  By identifying causal relationships, causal research provides empirical evidence to support policy decisions, program interventions, and business strategies. Decision-makers can use causal findings to allocate resources effectively and address societal challenges.
• Informing Predictive Modeling :  Causal research contributes to the development of predictive models by elucidating causal mechanisms underlying observed phenomena. Predictive models based on causal relationships can accurately forecast future outcomes and trends.
• Advancing Scientific Knowledge:  Causal research contributes to the cumulative body of scientific knowledge by testing hypotheses, refining theories, and uncovering underlying mechanisms of phenomena. It fosters a deeper understanding of complex systems and phenomena.
• Mitigating Confounding Factors:  Understanding causal relationships allows researchers to control for confounding variables and reduce bias in their studies. By isolating the effects of specific variables, researchers can draw more valid and reliable conclusions.

Causal Research Distinction from Other Research

Understanding the distinctions between causal research and other types of research methodologies is essential for researchers to choose the most appropriate approach for their study objectives. Let's explore the differences and similarities between causal research and descriptive, exploratory, and correlational research methodologies .

Descriptive vs. Causal Research

Descriptive research  focuses on describing characteristics, behaviors, or phenomena without manipulating variables or establishing causal relationships. It provides a snapshot of the current state of affairs but does not attempt to explain why certain phenomena occur.

Causal research , on the other hand, seeks to identify cause-and-effect relationships between variables by systematically manipulating independent variables and observing their effects on dependent variables. Unlike descriptive research, causal research aims to determine whether changes in one variable directly cause changes in another variable.

Similarities:

• Both descriptive and causal research involve empirical observation and data collection.
• Both types of research contribute to the scientific understanding of phenomena, albeit through different approaches.

Differences:

• Descriptive research focuses on describing phenomena, while causal research aims to explain why phenomena occur by identifying causal relationships.
• Descriptive research typically uses observational methods, while causal research often involves experimental designs or causal inference techniques to establish causality.

Exploratory vs. Causal Research

Exploratory research  aims to explore new topics, generate hypotheses, or gain initial insights into phenomena. It is often conducted when little is known about a subject and seeks to generate ideas for further investigation.

Causal research , on the other hand, is concerned with testing hypotheses and establishing cause-and-effect relationships between variables. It builds on existing knowledge and seeks to confirm or refute causal hypotheses through systematic investigation.

• Both exploratory and causal research contribute to the generation of knowledge and theory development.
• Both types of research involve systematic inquiry and data analysis to answer research questions.
• Exploratory research focuses on generating hypotheses and exploring new areas of inquiry, while causal research aims to test hypotheses and establish causal relationships.
• Exploratory research is more flexible and open-ended, while causal research follows a more structured and hypothesis-driven approach.

Correlational vs. Causal Research

Correlational research  examines the relationship between variables without implying causation. It identifies patterns of association or co-occurrence between variables but does not establish the direction or causality of the relationship.

Causal research , on the other hand, seeks to establish cause-and-effect relationships between variables by systematically manipulating independent variables and observing their effects on dependent variables. It goes beyond mere association to determine whether changes in one variable directly cause changes in another variable.

• Both correlational and causal research involve analyzing relationships between variables.
• Both types of research contribute to understanding the nature of associations between variables.
• Correlational research focuses on identifying patterns of association, while causal research aims to establish causal relationships.
• Correlational research does not manipulate variables, while causal research involves systematically manipulating independent variables to observe their effects on dependent variables.

How to Formulate Causal Research Hypotheses?

Crafting research questions and hypotheses is the foundational step in any research endeavor. Defining your variables clearly and articulating the causal relationship you aim to investigate is essential. Let's explore this process further.

1. Identify Variables

Identifying variables involves recognizing the key factors you will manipulate or measure in your study. These variables can be classified into independent, dependent, and confounding variables.

• Independent Variable (IV):  This is the variable you manipulate or control in your study. It is the presumed cause that you want to test.
• Dependent Variable (DV):  The dependent variable is the outcome or response you measure. It is affected by changes in the independent variable.
• Confounding Variables:  These are extraneous factors that may influence the relationship between the independent and dependent variables, leading to spurious correlations or erroneous causal inferences. Identifying and controlling for confounding variables is crucial for establishing valid causal relationships.

2. Establish Causality

Establishing causality requires meeting specific criteria outlined by scientific methodology. While correlation between variables may suggest a relationship, it does not imply causation. To establish causality, researchers must demonstrate the following:

• Temporal Precedence:  The cause must precede the effect in time. In other words, changes in the independent variable must occur before changes in the dependent variable.
• Covariation of Cause and Effect:  Changes in the independent variable should be accompanied by corresponding changes in the dependent variable. This demonstrates a consistent pattern of association between the two variables.
• Elimination of Alternative Explanations:  Researchers must rule out other possible explanations for the observed relationship between variables. This involves controlling for confounding variables and conducting rigorous experimental designs to isolate the effects of the independent variable.

3. Write Clear and Testable Hypotheses

Hypotheses serve as tentative explanations for the relationship between variables and provide a framework for empirical testing. A well-formulated hypothesis should be:

• Specific:  Clearly state the expected relationship between the independent and dependent variables.
• Testable:  The hypothesis should be capable of being empirically tested through observation or experimentation.
• Falsifiable:  There should be a possibility of proving the hypothesis false through empirical evidence.

For example, a hypothesis in a study examining the effect of exercise on weight loss could be: "Increasing levels of physical activity (IV) will lead to greater weight loss (DV) among participants (compared to those with lower levels of physical activity)."

By formulating clear hypotheses and operationalizing variables, researchers can systematically investigate causal relationships and contribute to the advancement of scientific knowledge.

Causal Research Design

Designing your research study involves making critical decisions about how you will collect and analyze data to investigate causal relationships.

Experimental vs. Observational Designs

One of the first decisions you'll make when designing a study is whether to employ an experimental or observational design. Each approach has its strengths and limitations, and the choice depends on factors such as the research question, feasibility , and ethical considerations.

• Experimental Design: In experimental designs, researchers manipulate the independent variable and observe its effects on the dependent variable while controlling for confounding variables. Random assignment to experimental conditions allows for causal inferences to be drawn. Example: A study testing the effectiveness of a new teaching method on student performance by randomly assigning students to either the experimental group (receiving the new teaching method) or the control group (receiving the traditional method).
• Observational Design: Observational designs involve observing and measuring variables without intervention. Researchers may still examine relationships between variables but cannot establish causality as definitively as in experimental designs. Example: A study observing the association between socioeconomic status and health outcomes by collecting data on income, education level, and health indicators from a sample of participants.

Control and Randomization

Control and randomization are crucial aspects of experimental design that help ensure the validity of causal inferences.

• Control: Controlling for extraneous variables involves holding constant factors that could influence the dependent variable, except for the independent variable under investigation. This helps isolate the effects of the independent variable. Example: In a medication trial, controlling for factors such as age, gender, and pre-existing health conditions ensures that any observed differences in outcomes can be attributed to the medication rather than other variables.
• Randomization: Random assignment of participants to experimental conditions helps distribute potential confounders evenly across groups, reducing the likelihood of systematic biases and allowing for causal conclusions. Example: Randomly assigning patients to treatment and control groups in a clinical trial ensures that both groups are comparable in terms of baseline characteristics, minimizing the influence of extraneous variables on treatment outcomes.

Internal and External Validity

Two key concepts in research design are internal validity and external validity, which relate to the credibility and generalizability of study findings, respectively.

• Internal Validity: Internal validity refers to the extent to which the observed effects can be attributed to the manipulation of the independent variable rather than confounding factors. Experimental designs typically have higher internal validity due to their control over extraneous variables. Example: A study examining the impact of a training program on employee productivity would have high internal validity if it could confidently attribute changes in productivity to the training intervention.
• External Validity: External validity concerns the extent to which study findings can be generalized to other populations, settings, or contexts. While experimental designs prioritize internal validity, they may sacrifice external validity by using highly controlled conditions that do not reflect real-world scenarios. Example: Findings from a laboratory study on memory retention may have limited external validity if the experimental tasks and conditions differ significantly from real-life learning environments.

Types of Experimental Designs

Several types of experimental designs are commonly used in causal research, each with its own strengths and applications.

• Randomized Control Trials (RCTs): RCTs are considered the gold standard for assessing causality in research. Participants are randomly assigned to experimental and control groups, allowing researchers to make causal inferences. Example: A pharmaceutical company testing a new drug's efficacy would use an RCT to compare outcomes between participants receiving the drug and those receiving a placebo.
• Quasi-Experimental Designs: Quasi-experimental designs lack random assignment but still attempt to establish causality by controlling for confounding variables through design or statistical analysis . Example: A study evaluating the effectiveness of a smoking cessation program might compare outcomes between participants who voluntarily enroll in the program and a matched control group of non-enrollees.

By carefully selecting an appropriate research design and addressing considerations such as control, randomization, and validity, researchers can conduct studies that yield credible evidence of causal relationships and contribute valuable insights to their field of inquiry.

Causal Research Data Collection

Collecting data is a critical step in any research study, and the quality of the data directly impacts the validity and reliability of your findings.

Choosing Measurement Instruments

Selecting appropriate measurement instruments is essential for accurately capturing the variables of interest in your study. The choice of measurement instrument depends on factors such as the nature of the variables, the target population , and the research objectives.

• Surveys :  Surveys are commonly used to collect self-reported data on attitudes, opinions, behaviors, and demographics . They can be administered through various methods, including paper-and-pencil surveys, online surveys, and telephone interviews.
• Observations:  Observational methods involve systematically recording behaviors, events, or phenomena as they occur in natural settings. Observations can be structured (following a predetermined checklist) or unstructured (allowing for flexible data collection).
• Psychological Tests:  Psychological tests are standardized instruments designed to measure specific psychological constructs, such as intelligence, personality traits, or emotional functioning. These tests often have established reliability and validity.
• Physiological Measures:  Physiological measures, such as heart rate, blood pressure, or brain activity, provide objective data on bodily processes. They are commonly used in health-related research but require specialized equipment and expertise.
• Existing Databases:  Researchers may also utilize existing datasets, such as government surveys, public health records, or organizational databases, to answer research questions. Secondary data analysis can be cost-effective and time-saving but may be limited by the availability and quality of data.

Ensuring accurate data collection is the cornerstone of any successful research endeavor. With the right tools in place, you can unlock invaluable insights to drive your causal research forward. From surveys to tests, each instrument offers a unique lens through which to explore your variables of interest.

At Appinio , we understand the importance of robust data collection methods in informing impactful decisions. Let us empower your research journey with our intuitive platform, where you can effortlessly gather real-time consumer insights to fuel your next breakthrough.   Ready to take your research to the next level? Book a demo today and see how Appinio can revolutionize your approach to data collection!

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Sampling Techniques

Sampling involves selecting a subset of individuals or units from a larger population to participate in the study. The goal of sampling is to obtain a representative sample that accurately reflects the characteristics of the population of interest.

• Probability Sampling:  Probability sampling methods involve randomly selecting participants from the population, ensuring that each member of the population has an equal chance of being included in the sample. Common probability sampling techniques include simple random sampling , stratified sampling, and cluster sampling .
• Non-Probability Sampling:  Non-probability sampling methods do not involve random selection and may introduce biases into the sample. Examples of non-probability sampling techniques include convenience sampling, purposive sampling, and snowball sampling.

The choice of sampling technique depends on factors such as the research objectives, population characteristics, resources available, and practical constraints. Researchers should strive to minimize sampling bias and maximize the representativeness of the sample to enhance the generalizability of their findings.

Ethical Considerations

Ethical considerations are paramount in research and involve ensuring the rights, dignity, and well-being of research participants. Researchers must adhere to ethical principles and guidelines established by professional associations and institutional review boards (IRBs).

• Informed Consent:  Participants should be fully informed about the nature and purpose of the study, potential risks and benefits, their rights as participants, and any confidentiality measures in place. Informed consent should be obtained voluntarily and without coercion.
• Privacy and Confidentiality:  Researchers should take steps to protect the privacy and confidentiality of participants' personal information. This may involve anonymizing data, securing data storage, and limiting access to identifiable information.
• Minimizing Harm:  Researchers should mitigate any potential physical, psychological, or social harm to participants. This may involve conducting risk assessments, providing appropriate support services, and debriefing participants after the study.
• Respect for Participants:  Researchers should respect participants' autonomy, diversity, and cultural values. They should seek to foster a trusting and respectful relationship with participants throughout the research process.
• Publication and Dissemination:  Researchers have a responsibility to accurately report their findings and acknowledge contributions from participants and collaborators. They should adhere to principles of academic integrity and transparency in disseminating research results.

By addressing ethical considerations in research design and conduct, researchers can uphold the integrity of their work, maintain trust with participants and the broader community, and contribute to the responsible advancement of knowledge in their field.

Causal Research Data Analysis

Once data is collected, it must be analyzed to draw meaningful conclusions and assess causal relationships.

Causal Inference Methods

Causal inference methods are statistical techniques used to identify and quantify causal relationships between variables in observational data. While experimental designs provide the most robust evidence for causality, observational studies often require more sophisticated methods to account for confounding factors.

• Difference-in-Differences (DiD):  DiD compares changes in outcomes before and after an intervention between a treatment group and a control group, controlling for pre-existing trends. It estimates the average treatment effect by differencing the changes in outcomes between the two groups over time.
• Instrumental Variables (IV):  IV analysis relies on instrumental variables—variables that affect the treatment variable but not the outcome—to estimate causal effects in the presence of endogeneity. IVs should be correlated with the treatment but uncorrelated with the error term in the outcome equation.
• Regression Discontinuity (RD):  RD designs exploit naturally occurring thresholds or cutoff points to estimate causal effects near the threshold. Participants just above and below the threshold are compared, assuming that they are similar except for their proximity to the threshold.
• Propensity Score Matching (PSM):  PSM matches individuals or units based on their propensity scores—the likelihood of receiving the treatment—creating comparable groups with similar observed characteristics. Matching reduces selection bias and allows for causal inference in observational studies.

Assessing Causality Strength

Assessing the strength of causality involves determining the magnitude and direction of causal effects between variables. While statistical significance indicates whether an observed relationship is unlikely to occur by chance, it does not necessarily imply a strong or meaningful effect.

• Effect Size:  Effect size measures the magnitude of the relationship between variables, providing information about the practical significance of the results. Standard effect size measures include Cohen's d for mean differences and odds ratios for categorical outcomes.
• Confidence Intervals:  Confidence intervals provide a range of values within which the actual effect size is likely to lie with a certain degree of certainty. Narrow confidence intervals indicate greater precision in estimating the true effect size.
• Practical Significance:  Practical significance considers whether the observed effect is meaningful or relevant in real-world terms. Researchers should interpret results in the context of their field and the implications for stakeholders.

Handling Confounding Variables

Confounding variables are extraneous factors that may distort the observed relationship between the independent and dependent variables, leading to spurious or biased conclusions. Addressing confounding variables is essential for establishing valid causal inferences.

• Statistical Control:  Statistical control involves including confounding variables as covariates in regression models to partially out their effects on the outcome variable. Controlling for confounders reduces bias and strengthens the validity of causal inferences.
• Matching:  Matching participants or units based on observed characteristics helps create comparable groups with similar distributions of confounding variables. Matching reduces selection bias and mimics the randomization process in experimental designs.
• Sensitivity Analysis:  Sensitivity analysis assesses the robustness of study findings to changes in model specifications or assumptions. By varying analytical choices and examining their impact on results, researchers can identify potential sources of bias and evaluate the stability of causal estimates.
• Subgroup Analysis:  Subgroup analysis explores whether the relationship between variables differs across subgroups defined by specific characteristics. Identifying effect modifiers helps understand the conditions under which causal effects may vary.

By employing rigorous causal inference methods, assessing the strength of causality, and addressing confounding variables, researchers can confidently draw valid conclusions about causal relationships in their studies, advancing scientific knowledge and informing evidence-based decision-making.

Causal Research Examples

Examples play a crucial role in understanding the application of causal research methods and their impact across various domains. Let's explore some detailed examples to illustrate how causal research is conducted and its real-world implications:

Example 1: Software as a Service (SaaS) User Retention Analysis

Suppose a SaaS company wants to understand the factors influencing user retention and engagement with their platform. The company conducts a longitudinal observational study, collecting data on user interactions, feature usage, and demographic information over several months.

• Design:  The company employs an observational cohort study design, tracking cohorts of users over time to observe changes in retention and engagement metrics. They use analytics tools to collect data on user behavior , such as logins, feature usage, session duration, and customer support interactions.
• Data Collection:  Data is collected from the company's platform logs, customer relationship management (CRM) system, and user surveys. Key metrics include user churn rates, active user counts, feature adoption rates, and Net Promoter Scores ( NPS ).
• Analysis:  Using statistical techniques like survival analysis and regression modeling, the company identifies factors associated with user retention, such as feature usage patterns, onboarding experiences, customer support interactions, and subscription plan types.
• Findings: The analysis reveals that users who engage with specific features early in their lifecycle have higher retention rates, while those who encounter usability issues or lack personalized onboarding experiences are more likely to churn. The company uses these insights to optimize product features, improve onboarding processes, and enhance customer support strategies to increase user retention and satisfaction.

Example 2: Business Impact of Digital Marketing Campaign

Consider a technology startup launching a digital marketing campaign to promote its new product offering. The company conducts an experimental study to evaluate the effectiveness of different marketing channels in driving website traffic, lead generation, and sales conversions.

• Design:  The company implements an A/B testing design, randomly assigning website visitors to different marketing treatment conditions, such as Google Ads, social media ads, email campaigns, or content marketing efforts. They track user interactions and conversion events using web analytics tools and marketing automation platforms.
• Data Collection:  Data is collected on website traffic, click-through rates, conversion rates, lead generation, and sales revenue. The company also gathers demographic information and user feedback through surveys and customer interviews to understand the impact of marketing messages and campaign creatives .
• Analysis:  Utilizing statistical methods like hypothesis testing and multivariate analysis, the company compares key performance metrics across different marketing channels to assess their effectiveness in driving user engagement and conversion outcomes. They calculate return on investment (ROI) metrics to evaluate the cost-effectiveness of each marketing channel.
• Findings:  The analysis reveals that social media ads outperform other marketing channels in generating website traffic and lead conversions, while email campaigns are more effective in nurturing leads and driving sales conversions. Armed with these insights, the company allocates marketing budgets strategically, focusing on channels that yield the highest ROI and adjusting messaging and targeting strategies to optimize campaign performance.

These examples demonstrate the diverse applications of causal research methods in addressing important questions, informing policy decisions, and improving outcomes in various fields. By carefully designing studies, collecting relevant data, employing appropriate analysis techniques, and interpreting findings rigorously, researchers can generate valuable insights into causal relationships and contribute to positive social change.

How to Interpret Causal Research Results?

Interpreting and reporting research findings is a crucial step in the scientific process, ensuring that results are accurately communicated and understood by stakeholders.

Interpreting Statistical Significance

Statistical significance indicates whether the observed results are unlikely to occur by chance alone, but it does not necessarily imply practical or substantive importance. Interpreting statistical significance involves understanding the meaning of p-values and confidence intervals and considering their implications for the research findings.

• P-values:  A p-value represents the probability of obtaining the observed results (or more extreme results) if the null hypothesis is true. A p-value below a predetermined threshold (typically 0.05) suggests that the observed results are statistically significant, indicating that the null hypothesis can be rejected in favor of the alternative hypothesis.
• Confidence Intervals:  Confidence intervals provide a range of values within which the true population parameter is likely to lie with a certain degree of confidence (e.g., 95%). If the confidence interval does not include the null value, it suggests that the observed effect is statistically significant at the specified confidence level.

Interpreting statistical significance requires considering factors such as sample size, effect size, and the practical relevance of the results rather than relying solely on p-values to draw conclusions.

Discussing Practical Significance

While statistical significance indicates whether an effect exists, practical significance evaluates the magnitude and meaningfulness of the effect in real-world terms. Discussing practical significance involves considering the relevance of the results to stakeholders and assessing their impact on decision-making and practice.

• Effect Size:  Effect size measures the magnitude of the observed effect, providing information about its practical importance. Researchers should interpret effect sizes in the context of their field and the scale of measurement (e.g., small, medium, or large effect sizes).
• Contextual Relevance:  Consider the implications of the results for stakeholders, policymakers, and practitioners. Are the observed effects meaningful in the context of existing knowledge, theory, or practical applications? How do the findings contribute to addressing real-world problems or informing decision-making?

Discussing practical significance helps contextualize research findings and guide their interpretation and application in practice, beyond statistical significance alone.

Addressing Limitations and Assumptions

No study is without limitations, and researchers should transparently acknowledge and address potential biases, constraints, and uncertainties in their research design and findings.

• Methodological Limitations:  Identify any limitations in study design, data collection, or analysis that may affect the validity or generalizability of the results. For example, sampling biases , measurement errors, or confounding variables.
• Assumptions:  Discuss any assumptions made in the research process and their implications for the interpretation of results. Assumptions may relate to statistical models, causal inference methods, or theoretical frameworks underlying the study.
• Alternative Explanations:  Consider alternative explanations for the observed results and discuss their potential impact on the validity of causal inferences. How robust are the findings to different interpretations or competing hypotheses?

Addressing limitations and assumptions demonstrates transparency and rigor in the research process, allowing readers to critically evaluate the validity and reliability of the findings.

Communicating Findings Clearly

Effectively communicating research findings is essential for disseminating knowledge, informing decision-making, and fostering collaboration and dialogue within the scientific community.

• Clarity and Accessibility:  Present findings in a clear, concise, and accessible manner, using plain language and avoiding jargon or technical terminology. Organize information logically and use visual aids (e.g., tables, charts, graphs) to enhance understanding.
• Contextualization:  Provide context for the results by summarizing key findings, highlighting their significance, and relating them to existing literature or theoretical frameworks. Discuss the implications of the findings for theory, practice, and future research directions.
• Transparency:  Be transparent about the research process, including data collection procedures, analytical methods, and any limitations or uncertainties associated with the findings. Clearly state any conflicts of interest or funding sources that may influence interpretation.

By communicating findings clearly and transparently, researchers can facilitate knowledge exchange, foster trust and credibility, and contribute to evidence-based decision-making.

Causal Research Tips

When conducting causal research, it's essential to approach your study with careful planning, attention to detail, and methodological rigor. Here are some tips to help you navigate the complexities of causal research effectively:

• Define Clear Research Questions:  Start by clearly defining your research questions and hypotheses. Articulate the causal relationship you aim to investigate and identify the variables involved.
• Consider Alternative Explanations:  Be mindful of potential confounding variables and alternative explanations for the observed relationships. Take steps to control for confounders and address alternative hypotheses in your analysis.
• Prioritize Internal Validity:  While external validity is important for generalizability, prioritize internal validity in your study design to ensure that observed effects can be attributed to the manipulation of the independent variable.
• Use Randomization When Possible:  If feasible, employ randomization in experimental designs to distribute potential confounders evenly across experimental conditions and enhance the validity of causal inferences.
• Be Transparent About Methods:  Provide detailed descriptions of your research methods, including data collection procedures, analytical techniques, and any assumptions or limitations associated with your study.
• Utilize Multiple Methods:  Consider using a combination of experimental and observational methods to triangulate findings and strengthen the validity of causal inferences.
• Be Mindful of Sample Size:  Ensure that your sample size is adequate to detect meaningful effects and minimize the risk of Type I and Type II errors. Conduct power analyses to determine the sample size needed to achieve sufficient statistical power.
• Validate Measurement Instruments:  Validate your measurement instruments to ensure that they are reliable and valid for assessing the variables of interest in your study. Pilot test your instruments if necessary.
• Seek Feedback from Peers:  Collaborate with colleagues or seek feedback from peer reviewers to solicit constructive criticism and improve the quality of your research design and analysis.

Conclusion for Causal Research

Mastering causal research empowers researchers to unlock the secrets of cause and effect, shedding light on the intricate relationships between variables in diverse fields. By employing rigorous methods such as experimental designs, causal inference techniques, and careful data analysis, you can uncover causal mechanisms, predict outcomes, and inform evidence-based practices. Through the lens of causal research, complex phenomena become more understandable, and interventions become more effective in addressing societal challenges and driving progress. In a world where understanding the reasons behind events is paramount, causal research serves as a beacon of clarity and insight. Armed with the knowledge and techniques outlined in this guide, you can navigate the complexities of causality with confidence, advancing scientific knowledge, guiding policy decisions, and ultimately making meaningful contributions to our understanding of the world.

How to Conduct Causal Research in Minutes?

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What is Convenience Sampling? Definition, Method, Examples

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5 Cause and effect: The epidemiological approach

• Published: September 2016
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Cause and effect understanding is the highest form of scientific knowledge. In epidemiology, demonstrating causality is difficult because of the long and complex natural history of many human diseases and because of ethical restraints. Epidemiologists should: hold the attitude that all judgements of cause and effect are tentative; understand that causal thinking demands a judgement; be alert for the play of chance, error, and bias; always consider reverse causality and confounding, utilize the power of causal models that broaden causal perspectives; apply guidelines for causality as an aid to thinking and not as a checklist; and look for corroboration of causality from other scientific frameworks for assessment of cause and effect. The ultimate aim of epidemiology is to use knowledge of cause and effect to break links between disease and its causes and to improve health. The application of erroneous knowledge has serious repercussions.

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Experimental Method In Psychology

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

The experimental method involves the manipulation of variables to establish cause-and-effect relationships. The key features are controlled methods and the random allocation of participants into controlled and experimental groups .

What is an Experiment?

An experiment is an investigation in which a hypothesis is scientifically tested. An independent variable (the cause) is manipulated in an experiment, and the dependent variable (the effect) is measured; any extraneous variables are controlled.

An advantage is that experiments should be objective. The researcher’s views and opinions should not affect a study’s results. This is good as it makes the data more valid  and less biased.

There are three types of experiments you need to know:

1. Lab Experiment

A laboratory experiment in psychology is a research method in which the experimenter manipulates one or more independent variables and measures the effects on the dependent variable under controlled conditions.

A laboratory experiment is conducted under highly controlled conditions (not necessarily a laboratory) where accurate measurements are possible.

The researcher uses a standardized procedure to determine where the experiment will take place, at what time, with which participants, and in what circumstances.

Participants are randomly allocated to each independent variable group.

Examples are Milgram’s experiment on obedience and  Loftus and Palmer’s car crash study .

• Strength : It is easier to replicate (i.e., copy) a laboratory experiment. This is because a standardized procedure is used.
• Strength : They allow for precise control of extraneous and independent variables. This allows a cause-and-effect relationship to be established.
• Limitation : The artificiality of the setting may produce unnatural behavior that does not reflect real life, i.e., low ecological validity. This means it would not be possible to generalize the findings to a real-life setting.
• Limitation : Demand characteristics or experimenter effects may bias the results and become confounding variables .

2. Field Experiment

A field experiment is a research method in psychology that takes place in a natural, real-world setting. It is similar to a laboratory experiment in that the experimenter manipulates one or more independent variables and measures the effects on the dependent variable.

However, in a field experiment, the participants are unaware they are being studied, and the experimenter has less control over the extraneous variables .

Field experiments are often used to study social phenomena, such as altruism, obedience, and persuasion. They are also used to test the effectiveness of interventions in real-world settings, such as educational programs and public health campaigns.

An example is Holfing’s hospital study on obedience .

• Strength : behavior in a field experiment is more likely to reflect real life because of its natural setting, i.e., higher ecological validity than a lab experiment.
• Strength : Demand characteristics are less likely to affect the results, as participants may not know they are being studied. This occurs when the study is covert.
• Limitation : There is less control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.

3. Natural Experiment

A natural experiment in psychology is a research method in which the experimenter observes the effects of a naturally occurring event or situation on the dependent variable without manipulating any variables.

Natural experiments are conducted in the day (i.e., real life) environment of the participants, but here, the experimenter has no control over the independent variable as it occurs naturally in real life.

Natural experiments are often used to study psychological phenomena that would be difficult or unethical to study in a laboratory setting, such as the effects of natural disasters, policy changes, or social movements.

For example, Hodges and Tizard’s attachment research (1989) compared the long-term development of children who have been adopted, fostered, or returned to their mothers with a control group of children who had spent all their lives in their biological families.

Here is a fictional example of a natural experiment in psychology:

Researchers might compare academic achievement rates among students born before and after a major policy change that increased funding for education.

In this case, the independent variable is the timing of the policy change, and the dependent variable is academic achievement. The researchers would not be able to manipulate the independent variable, but they could observe its effects on the dependent variable.

• Strength : behavior in a natural experiment is more likely to reflect real life because of its natural setting, i.e., very high ecological validity.
• Strength : Demand characteristics are less likely to affect the results, as participants may not know they are being studied.
• Strength : It can be used in situations in which it would be ethically unacceptable to manipulate the independent variable, e.g., researching stress .
• Limitation : They may be more expensive and time-consuming than lab experiments.
• Limitation : There is no control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.

Key Terminology

Ecological validity.

The degree to which an investigation represents real-life experiences.

Experimenter effects

These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.

Demand characteristics

The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).

Independent variable (IV)

The variable the experimenter manipulates (i.e., changes) is assumed to have a direct effect on the dependent variable.

Dependent variable (DV)

Variable the experimenter measures. This is the outcome (i.e., the result) of a study.

Extraneous variables (EV)

All variables which are not independent variables but could affect the results (DV) of the experiment. EVs should be controlled where possible.

Confounding variables

Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

Random Allocation

Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of participating in each condition.

The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.

Order effects

Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:

(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;

(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.

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Statistics By Jim

Making statistics intuitive

Causation in Statistics: Hill’s Criteria

By Jim Frost 11 Comments

Causation indicates that an event affects an outcome. Do fatty diets cause heart problems? If you study for a test, does it cause you to get a higher score?

In statistics , causation is a bit tricky. As you’ve no doubt heard, correlation doesn’t necessarily imply causation. An association or correlation between variables simply indicates that the values vary together. It does not necessarily suggest that changes in one variable cause changes in the other variable. Proving causality can be difficult.

If correlation does not prove causation, what statistical test do you use to assess causality? That’s a trick question because no statistical analysis can make that determination. In this post, learn about why you want to determine causation and how to do that.

Relationships and Correlation vs. Causation

The expression is, “correlation does not imply causation.” Consequently, you might think that it applies to things like Pearson’s correlation coefficient . And, it does apply to that statistic. However, we’re really talking about relationships between variables in a broader context. Pearson’s is for two continuous variables . However, a relationship can involve different types of variables such as categorical variables , counts, binary data, and so on.

For example, in a medical experiment, you might have a categorical variable that defines which treatment group subjects belong to—control group, placebo group, and several different treatment groups. If the health outcome is a continuous variable, you can assess the differences between group means. If the means differ by group, then you can say that mean health outcomes depend on the treatment group. There’s a correlation, or relationship, between the type of treatment and health outcome. Or, maybe we have the treatment groups and the outcome is binary, say infected and not infected. In that case, we’d compare group proportions of the infected/not infected between groups to determine whether treatment correlates with infection rates.

Through this post, I’ll refer to correlation and relationships in this broader sense—not just literal correlation coefficients . But relationships between variables, such as differences between group means and proportions, regression coefficients , associations between pairs of categorical variables , and so on.

Why Determining Causality Is Important

If you’re only predicting events, not trying to understand why they happen, and do not want to alter the outcomes, correlation can be perfectly fine. For example, ice cream sales correlate with shark attacks. If you just need to predict the number of shark attacks, ice creams sales might be a good thing to measure even though it’s not causing the shark attacks.

However, if you want to reduce the number of attacks, you’ll need to find something that genuinely causes a change in the attacks. As far as I know, sharks don’t like ice cream!

There are many occasions where you want to affect the outcome. For example, you might want to do the following:

• Improve health by using medicine, exercising, or flu vaccinations .
• Reducing the risk of adverse outcomes, such as procedures for reducing manufacturing defects.
• Improving outcomes, such as studying for a test.

For intentional changes in one variable to affect the outcome variable, there must be a causal relationship between the variables. After all, if studying does not cause an increase in test scores, there’s no point for studying. If the medicine doesn’t cause an improvement in your health or ward off disease, there’s no reason to take it.

Before you can state that some course of action will improve your outcomes, you must be sure that a causal relationship exists between your variables.

Confounding Variables and Their Role in Causation

How does it come to be that variables are correlated but do not have a causal relationship? A common reason is a confounding variable that creates a spurious correlation. A confounding variable correlates with both of your variables of interest. It’s possible that the confounding variable might be the real causal factor ! Let’s go through the ice cream and shark attack example.

In this example, the number of people at the beach is a confounding variable. A confounding variable correlates with both variables of interest—ice cream and shark attacks in our example.

In the diagram below, imagine that as the number of people increases, ice cream sales also tend to increase. In turn, more people at the beach cause shark attacks to increase. The correlation structure creates an apparent, or spurious, correlation between ice cream sales and shark attacks, but it isn’t causation.

Confounders are common reasons for associations between variables that are not causally connected.

Related post : Confounding Variables Can Bias Your Results

Causation and Hypothesis Tests

Before moving on to determining whether a relationship is causal, let’s take a moment to reflect on why statistically significant hypothesis test results do not signify causation.

Hypothesis tests are inferential procedures . They allow you to use relatively small samples to draw conclusions about entire populations. For the topic of causation, we need to understand what statistical significance means.

When you see a relationship in sample data, whether it is a correlation coefficient, a difference between group means, or a regression coefficient, hypothesis tests help you determine whether your sample provides sufficient evidence to conclude that the relationship exists in the population . You can see it in your sample, but you need to know whether it exists in the population. It’s possible that random sampling error (i.e., luck of the draw) produced the “relationship” in your sample.

Statistical significance indicates that you have sufficient evidence to conclude that the relationship you observe in the sample also exists in the population.

That’s it. It doesn’t address causality at all.

Related post : Understanding P-values and Statistical Significance

Hill’s Criteria of Causation

Determining whether a causal relationship exists requires far more in-depth subject area knowledge and contextual information than you can include in a hypothesis test. In 1965, Austin Hill, a medical statistician, tackled this question in a paper* that’s become the standard. While he introduced it in the context of epidemiological research, you can apply the ideas to other fields.

Hill describes nine criteria to help establish causal connections. The goal is to satisfy as many criteria possible. No single criterion is sufficient. However, it’s often impossible to meet all the criteria. These criteria are an exercise in critical thought. They show you how to think about determining causation and highlight essential qualities to consider.

Studies can take steps to increase the strength of their case for a causal relationship, which statisticians call internal validity . To learn more about this, read my post about internal and external validity .

A strong, statistically significant relationship is more likely to be causal. The idea is that causal relationships are likely to produce statistical significance. If you have significant results, at the very least you have reason to believe that the relationship in your sample also exists in the population—which is a good thing. After all, if the relationship only appears in your sample, you don’t have anything meaningful! Correlation still does not imply causation, but a statistically significant relationship is a good starting point.

However, there are many more criteria to satisfy! There’s a critical caveat for this criterion as well. Confounding variables can mask a correlation that actually exists. They can also create the appearance of correlation where causation doesn’t exist, as shown with the ice cream and shark attack example. A strong relationship is simply a hint.

Consistency and causation

When there is a real, causal connection, the result should be repeatable. Other experimenters in other locations should be able to produce the same results. It’s not one and done. Replication builds up confidence that the relationship is causal. Preferably, the replication efforts use other methods, researchers, and locations.

In my post with five tips for using p-values without being misled , I emphasize the need for replication.

Specificity

It’s easier to determine that a relationship is causal if you can rule out other explanations. I write about ruling out other explanations in my posts about randomized experiments and observational studies. In a more general sense, it’s essential to study the literature, consider other plausible hypotheses, and, hopefully, be able to rule them out or otherwise control for them. You need to be sure that what you’re studying is causing the observed change rather than something else of which you’re unaware.

It’s important to note that you don’t need to prove that your variable of interest is the only factor that affects the outcome. For example, smoking causes lung cancer, but it’s not the only thing that causes it. However, you do need to perform experiments that account for other relevant factors and be able to attribute some causation to your variable of interest specifically.

For example, in regression analysis , you control for other factors by including them in the model .

Temporality and causation

Causes should precede effects. Ensure that what you consider to be the cause occurs before the effect . Sometimes it can be challenging to determine which way causality runs. Hill uses the following example. It’s possible that a particular diet leads to an abdominal disease. However, it’s also possible that the disease leads to specific dietary habits.

The Granger Causality Test assesses potential causality by determining whether earlier values in one time series predicts later values in another time series. Analysts say that time series A Granger-causes time series B when significant statistical tests indicate that values in series A predict future values of series B.

Despite being called a “causality test,” it really is only a test of prediction. After all, the increase of Christmas card sales Granger-causes Christmas!

Temporality is just one aspect of causality!

Biological Gradient

Hill was a biologist, hence the focus on biological questions. He suggests that for a genuinely causal relationship, there should be a dose-response type of relationship. If a little bit of exposure causes a little bit of change, a larger exposure should cause more change. Hill uses cigarette smoking and lung cancer as an example—greater amounts of smoking are linked to a greater risk of lung cancer. You can apply the same type of thinking in other fields. Does more studying lead to even higher scores?

However, be aware that the relationship might not remain linear. As the dose increases beyond a threshold, the response can taper off. You can check for this by modeling curvature in regression analysis .

Plausibility

If you can find a plausible mechanism that explains the causal nature of the relationship, it supports the notion of a causal relationship. For example, biologists understand how antibiotics inhibit microbes on a biological level. However, Hill points out that you have to be careful because there are limits to scientific knowledge at any given moment. A causal mechanism might not be known at the time of the study even if one exists. Consequently, Hill says, “we should not demand” that a study meets this requirement.

Coherence and causation

The probability that a relationship is causal is higher when it is consistent with related causal relationships that are generally known and accepted as facts. If your results outright disagree with accepted facts, it’s more likely to be correlation. Assess causality in the broader context of related theory and knowledge.

Experiments and causation

Randomized experiments are the best way to identify causal relationships. Experimenters control the treatment (or factors involved), randomly assign the subjects, and help manage other sources of variation. Hill calls satisfying this criterion the strongest support for causation. However, randomized experiments are not always possible as I write about in my post about observational studies. Learn more about Experimental Design: Definition, Types and Examples .

Related posts : Randomized Experiments and Observational Studies

If there is an accepted, causal relationship that is similar to a relationship in your research, it supports causation for the current study. Hill writes, “With the effects of thalidomide and rubella before us we would surely be ready to accept slighter but similar evidence with another drug or another viral disease in pregnancy.”

Determining whether a correlation also represents causation requires much deliberation. Properly designing experiments and using statistical procedures can help you make that determination. But there are many other factors to consider.

Use your critical thinking and subject-area expertise to think about the big picture. If there is a causal relationship, you’d expect to see consistent results that have been replicated, other causes have been ruled out, the results fit with established theory and other findings, there is a plausible mechanism, and the cause precedes the effect.

Austin Bradford Hill, “The Environment and Disease: Association or Causation?,” Proceedings of the Royal Society of Medicine , 58 (1965), 295-300.

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Reader Interactions

December 2, 2020 at 9:06 pm

I believe there is a logical flaw in the movie “Good Will Hunting”. Specifically, in the scene where psychologist Dr. Sean Maguire (Robin Williams) tells Will (Matt Damon) about the first time he met his wife, there seems to be an implied assumption that if Sean had gone to “the game” (Game 6 of the World Series in 1975), instead of staying at the bar where he had just met his future wife, then the very famous home run hit by Carlton Fisk would still have occurred. I contend that if Sean had gone to the game, the game would have played out completely differently, and the famous home run which actually occurred would not have occurred – that’s not to say that some other famous home run could not have occurred. It seems to be clear that neither characters Sean nor Will understand this – and I contend these two supposedly brilliant people would have known better! It is certainly clear that neither Matt Damon nor Ben Affleck (the writers) understand this. What do you think?

August 24, 2019 at 8:00 pm

Hi Jim Thanks for the great site and content. Being new to statistics I am finding it daunting to understand all of these concepts. I have read most of the articles in the basics section and whilst I am gaining some insights I feel like I need to take a step back in order to move forward. Could you recommend some resources for a rank beginner such as my self? Maybe some books that you read when you where starting out that where useful. I am really keen to jump in and start doing some statistics but I am wondering if it is even possible for someone like me to do so. To clearly define my question where is the best place to start?? I realize this doesn’t really relate to the above article but hopefully this question might be useful to others as well. Thanks.

August 25, 2019 at 2:45 pm

I’m glad that my website has been helpful! I do understand your desire to get the pick picture specifically for starting out. In just about a week, September 3rd to be exact, I’m launching a new ebook that does just that. The book is titled Introduction to Statistics: An Intuitive Guide for Analyzing Data and Unlocking Discoveries . My goal is to provide the big picture about the field of statistics. It covers the basics of data analysis up to larger issues such as using experiments and data to make discoveries.

To be sure that you receive the latest about this book, please subscribe to my email list using the form in the right column of every page in my website.

August 16, 2019 at 12:55 am

Jim , I am new to stats and find ur blog very useful. Yet , I am facing an issue of very low R square values , as low as 1 percent, 3 percent… do we still hold these values valid? Any references on research while accepting such low values . request ur valuable inputs please.

August 17, 2019 at 4:11 pm

Low R-squared can be a problem. It depends on several other factors. Are any independent variables significant? Is the F-test of overall significance significant?

I have posts about this topic and answers those questions. Please read: Low R-squared values and F-test of overall significance .

If you have further questions, please post them in the comments section of the relevant post. It helps keep the questions and answers organized for other readers. Thanks!

June 27, 2019 at 11:23 am

Thank you so much for your website. It has helped me tremendously with my stats, particularly regression. I have a question concerning correlation testing. I have a continuous dependent variable, quality of life, and 3 independent variables, which are categorical (education = 4 levels, marital status = 3 levels, stress = 3 levels). How can I test for a relationship among the dependent and independent variables? Thank you Jim.

June 27, 2019 at 1:30 pm

You can use either ANOVA or OLS regression to assess the relationship between categorical IVs to a continuous DV.

I write about this in my ebook, Regression Analysis: An Intuitive Guide . I recommend you get that ebook to learn about how it works with categorical IVs. I discuss that in detail in the ebook. Unfortunately, I don’t have a blog post to point you towards.

Best of luck with your analysis!

June 25, 2019 at 3:24 pm

great post, Jim. Thanks!

June 25, 2019 at 11:32 am

Useful post

June 24, 2019 at 4:51 am

Very nice and interesting post. And very educational. Many thanks for your efforts!

June 24, 2019 at 10:13 am

Thank you very much! I appreciate the kind words!

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Causation vs. Correlation Explained With 10 Examples

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You would think by now that we could say unequivocally what causes what. But the question of causation vs. correlation , which has haunted science and philosophy from their earliest days, still dogs our heels for numerous reasons.

Humans are evolutionarily predisposed to see patterns, and psychologically inclined to gather information that supports preexisting views, a trait known as confirmation bias. We confuse coincidence with correlation, and correlation with causality.

What's the Difference Between Causation and Correlation?

Measuring correlation, 10 examples of correlation and causation.

The difference between causation and correlation is that in a causal relationship, one event is directly responsible for another, while in a correlation, two events exist simultaneously, but their relationship may be due to a third variable.

It's incorrect to say that correlation implies causation. For A to cause B, we tend to say that, at a minimum:

• A must precede B.
• The two must covary (vary together)
• No competing explanation can better explain the covariance of A and B.

Taken alone, however, these three requirements cannot prove cause; they are, as philosophers say, necessary but not sufficient. In any case, not everyone agrees with them.

Speaking of philosophers, David Hume argued that causation doesn't exist in any provable sense [source: Cook]. Karl Popper and the Falsificationists maintained that we cannot prove a relationship, only disprove it, which explains why statistical analyses do not try to prove a correlation; instead, they pull a double negative and disprove that the data are uncorrelated, a process known as rejecting the null hypothesis [source: McLeod ].

With such considerations in mind, scientists must carefully design and control their experiments to weed out bias, circular reasoning, self-fulfilling prophecies and confounding variables. They must respect the requirements and limitations of the methods used, draw from representative samples where possible and not overstate their results.

Instead of undertaking the difficult (and maybe impossible) task of establishing causality, most scientific research focuses on the strength of correlations. Correlations can be positive or negative, weak or strong. The statistical correlation coefficient, which ranges from -1 to 1, shows the strength and direction of the correlation.

If you plot data points on a graph where one variable occupies the X-axis and another occupies the Y-axis, the variables correlate if they have a linear relationship.

• In a positive correlation , two variables move in the same direction. When one variable increases, the other also increases. In a perfect positive correlation, the correlation coefficient is 1.
• In a negative correlation , two variables move in opposite directions. Increasing one variable decreases the other. The correlation coefficient is a negative number between 0 and -1.
• There is zero correlation if the data points are all over the graph instead of forming a straight line. The correlation coefficient will be 0.

Because the human brain tends to seek out causal relationships, scientists are extra careful about creating highly controlled experiments — but they still make mistakes. Here are ten examples illustrating how hard it is to identify causation.

10. The Trouble With Henry (and Hawthorne)

People are a pain to research. They react not only to the stimulus being studied, but also to the experiment itself. Researchers today try to design experiments to control for such factors, but that wasn't always the case.

Take the Hawthorne Works in Cicero, Illinois. In a series of experiments from 1924 to 1932, researchers studied worker productivity effects associated with altering the Illinois factory's environment, including changing light levels, tidying up the place and moving workstations around.

Just when they thought they were on to something, they noticed a problem: The observed increases in productivity dropped almost as soon as the researchers left the works, indicating that the workers' knowledge of the experiment — not the researchers' changes — had fueled the boost. Researchers still call this phenomenon the Hawthorne Effect [source: Obrenović ].

A related concept, the John Henry effect, occurs when members of a control group try to beat the experimental group by kicking their efforts into overdrive. They need not know about the experiment; they need only see one group receive new tools or additional instruction. Like the steel-driving man of legend, they want to prove their capabilities and earn respect [sources: Saretsky; Vogt ].

9. Always Bet on Black?

The titular characters of Tom Stoppard's film " Rosencrantz and Guildenstern Are Dead " begin the film baffled and finally frightened as each of 157 consecutive flips of a coin comes up heads. Guildenstern's explanations of this phenomenon range from time loops to "a spectacular vindication of the principle that each individual coin, spun individually, is as likely to come down heads as tails ... "

Evolution wired humans to see patterns, and our ability to properly process that urge seems to short-circuit the longer we spend gambling . We can rationally accept that independent events like coin flips keep the same odds no matter how many times you perform them.

But we also view those events, less rationally, as streaks, making false mental correlations between randomized events. Viewing the past as prelude, we keep thinking the next flip ought to be tails.

Statisticians call this the gambler's fallacy, aka the Monte Carlo fallacy, after a particularly illustrative example that occurred in that famed Monaco resort town.

During the summer of 1913, bettors watched in increasing amazement as a casino's roulette wheel landed on black 26 times in a row. Inflamed by the certainty that red was "due," the punters kept plunking down their chips. The casino made a mint [sources: Lehrer; Oppenheimer and Monin ; Vogt ].

8. The Hot Hand and the Monkey's Paw

No discussion of streaks, magical thinking or false causation would be complete without a flip through the sports pages. Stellar sports seasons arise from such a mysterious interplay of factors — natural ability, training, confidence, the occasional X factor — that we imagine patterns in performance, even though studies repeatedly reject streak shooting and "successful" superstitions as anything more than imaginary.

The belief in streaks or slumps implies that success "causes" success and failure "causes" failure or, perhaps more reasonably, that variation in some common factor, such as confidence, causes both. But study after study fails to bear this out [source: Gilovich, et al ].

The same holds true for superstitions , although that never stopped retired NBA player and Dallas Mavericks guard Jason Terry from sleeping in the opposing teams' game shorts before each game, or NHL center and retired Ottawa Senators player Bruce Gardiner from dunking his hockey stick in the toilet to break the occasional slump [source: Exact Sports ].

The sophomore slump, too, typically arises from a too-good first year. Performance swings tend to even out in the long run, a phenomenon statisticians call regression toward the mean [source: Barnett, et al ]. In sports, this averaging out is aided by the opposition, which adjusts to counter the new player's successful skill set.

7. Hormonal Imbalance

Randomized controlled trials are the gold standard in statistics, but sometimes — in epidemiology , for example — ethical and practical considerations force researchers to analyze available cases.

Unfortunately, such observational studies risk bias, hidden variables and, worst of all, study groups that might not accurately reflect the population. Studying a representative sample is vital; it allows researchers to apply results to people outside of the study, like the rest of us.

A case in point: hormone replacement therapy (HRT) for women. Beyond treating symptoms associated with menopause, it was once hailed for potentially reducing coronary heart disease (CHD) risk, thanks to a much-ballyhooed 1991 observational study [source: Stampfer and Colditz ].

But later randomized controlled studies, including the large-scale Women's Health Initiative , revealed either a negative relationship, or a statistically insignificant one, between HRT and CHD [source: Lawlor, et al. ].

Why the difference? For one thing, women who use HRT tend to come from higher socioeconomic strata and receive better quality of diet and exercise — a hidden explanatory relationship for which the observational study failed to fully account [source: Lawlor, et al ].

6. Super Bowl Stock Market Shuffle

In 1978, sports reporter and columnist Leonard Koppett mocked the causation-correlation confusion by wryly suggesting that Super Bowl outcomes could predict the stock market. It backfired: Not only did people believe him, but it worked — with frightful frequency.

The proposal, now commonly known as the Super Bowl Indicator, went as follows: If one of the 16 original National Football League teams — those in existence before the NFL's 1966 merger with the American Football League — won the Super Bowl , the stock market would rise throughout the rest of the year. If a former AFL team won, it would go down [source: Bonsal ].

From 1967 to 1978, Koppett's system went 12 for 12; up through 1997, it boasted a 95 percent success rate. It stumbled during the dot-com era (1998–2001) and notably in 2008, when the Great Recession hit, despite a win by the New York Giants (NFC). Still, as of 2022, the indicator had a 73 percent success rate [source: Chen ].

Some have argued that the pattern exists, driven by belief; it works, they say, because investors believe it does, or because they believe that other investors believe it.

This notion, though clever in a regressive sort of way, hardly explains the 12 years of successful correlations predating Koppett's article. Others argue that a more relevant pattern lies in the stock market's large-scale upward trend, barring some short-term major and minor fluctuations [source: Johnson ].

5. Big Data, Little Clarity

Big data — the process of looking for patterns in data sets so large they resist traditional methods of analysis — rates big buzz in the boardroom [source: Arthur ]. But is bigger always better?

It's a rule that's drummed into most researchers in their first stats class: When encountering a sea of data, resist the urge to go on a fishing expedition. Given enough data, patience and methodological leeway, correlations are almost inevitable, if unethical and largely useless.

After all, the mere correlation between two variables does not imply causation; nor does it, in many cases, point to much of a relationship.

For one thing, researchers cannot use statistical measures of correlation willy-nilly; each contains certain assumptions and limitations that fishing expeditions too often ignore, to say nothing of the hidden variables, sampling problems and flaws in interpretation that can gum up a poorly designed study.

But big data is increasingly being used and hailed for its invaluable contributions to areas such as creating customized learning programs; wearable devices that provide real-time feed to your electronic health records; and music streaming services that give you targeted recommendations [source: IntelliPaat ]. Just don't expect too much out of big data in the causality department.

4. Minimum Wage Equals Maximum Unemployment

Any issue dealing with money is bound to be deeply divisive and highly politicized, and minimum wage increases are no exception. The arguments are varied and complex, but essentially one side contends that a higher minimum wage hurts businesses, which drives down job availability, which hurts the poor.

The other side responds that there's little evidence for this claim, and that the 76 million Americans working at or below minimum wage, which some argue is not a living wage, would benefit from such an increase. They argue that the federal minimum wage for covered, nonexempt employees ($7.25 per hour in September 2023) has lowered Americans' purchasing power by more than 20 percent [sources: U.S. Department of Labor ; Cooper, et al ].

As literary critic George Shaw reportedly quipped, "If all the economists were laid end to end, they'd never reach a conclusion," and the minimum-wage debate seems to bear that out [source: Quote Investigator ]. For every analyst who says minimum wage increases drive jobs away, there is another who argues against such a correlation.

In the end, both sides share a fundamental problem: namely, the abundance of anecdotal evidence many of their talking heads rely on for support. Secondhand stories and cherry-picked data make for weak tea in any party, even when presented in pretty bar charts.

3. Breakfast Beats Obesity, Dinner Denies Drugs

Between fitness apps, drugs and surgeries, weight loss in the United States is a $78 billion-per-year industry, with millions of Americans bellying up to the weight-loss bar annually [source: Research and Markets ]. Not surprisingly, weight loss studies — good, bad or ugly — get a lot of press in the U.S.

Take the popular idea that eating breakfast beats obesity, a sugar-frosted nugget derived from two main studies: One, a 1992 Vanderbilt University randomized controlled study , showed that reversing normal breakfast habits, whether by eating or not eating, correlated with weight loss; the other, a 2002 observational study by the National Weight Control Registry, correlated breakfast-eating with successful weight-losers — which is not the same as correlating it with weight loss [sources: Brown, et al. ; Schlundt, et al.; Wyatt, et al.].

Unfortunately, the NWCR study failed to control for other factors — or, indeed, establish any causal connection from its correlation. For example, a person who wants to lose weight might work out more, eat breakfast or go whole-hog protein, but without an experimental design capable of dialing in causal links, such behaviors amount to nothing more than commonly co-occurring characteristics [source: Brown, et al ].

A similar problem plagues the numerous studies linking family dinners with a decreased risk of drug addiction for teens. Although attractive for their simple, appealing strategy, these studies frequently fail to control for related factors, such as strong family connections or deep parental involvement in a child's life [source: Miller, et al].

2. The Suicidal Sex

We often hear that men, especially young men, are more likely to commit suicide than are women. In truth, such statements partake of empirical generalization — the act of making a broad statement about a common pattern without attempting to explain it — and mask several known and potential confounding factors.

Take, for example, a Youth Risk Behaviors Survey from 2021 found that girls in grades 9-12 attempted suicide almost twice as often as male students (13 percent vs. 7 percent) [source: American Foundation for Suicide Prevention ].

How, then, can a higher correlation exist between the opposite sex and suicide? The answer lies in suicide attempts by methodology: While the most common method of suicide for both sexes in 2020 was by firearm (57.9 percent for men and 33.0 percent for women), women were almost equally likely to die by poisoning or suffocation [source: National Institute of Mental Health ].

Even if we could dispose of such confounding factors, the fact would remain that maleness, per se, is not a cause. To explain the trend, we need to instead identify factors common to men, or at least suicidal ones.

The same point applies to the comparatively high rates of suicide reported among divorced men. Divorce doesn't cause men to commit suicide; if anything, it's more indicative of an underlying causal relationship with factors such as male role inflexibility, their social networks, the increasing importance of child care and men's desire for control in relationships [source: Scourfield and Evans ].

1. Vaccination Vexation

No correlation/causation list would be complete without discussing parental concerns over vaccination safety . Before the COVID-19 pandemic hit the world in 2020, the main issue was a fear among some parents that the measles, mumps and rubella vaccination was causally linked to autism spectrum disorders. This notion was popularized by celebrities like Jenny McCarthy.

Despite the medical community debunking the 1998 Andrew Wakefield paper that inspired the falsehood, and despite subsequent studies showing no causal link, some parents remain fearful of an autism connection or other vaccine-related dangers [sources: Park ; Sifferlin ; Szabo ].

Then COVID-19 arrived, and to date has killed millions around the globe. Scientists raced to create an effective vaccine and they succeeded; the first U.S. COVID-19 vaccine was available in December 2020 under the FDA's emergency use authorization [source: FDA ]. But it also quickly became intertwined with the extreme polarization of U.S. politics and misinformation.

Many parents, especially Republicans, feared the vaccines were unsafe because they were developed so quickly, and because there might be as-yet-unknown long-term side effects. There were also incorrect fears about the vaccine affecting future fertility. Those have now been proven false [source: Kelen and Maragakis ].

As of January 2022, just 28 percent of 5- to 11-year-olds had received at least one dose of the vaccine, disappointing many in the medical field [sources: Hamel , Kates ]. The number of vaccinated children is growing; by May 2023, 40 percent of 5- to 11-year-olds had received at least done dose [source: CDC ].

These are no harmless misunderstandings. Despite debunking a link between autism and childhood vaccines, many parents remain leery of the shots. In 2019, there were 1,282 cases of measles in 31 states, the highest number in the U.S. since 1992. The majority of these cases were among the unvaccinated [source: CDC ].

Whether that correspondence is coincidental, correlative or causal is well worth considering. And the effects of the current COVID-19 vaccination hesitation remain to be seen.

Lots More Information

Related articles.

• Are Stupid People Happier?
• Do Countries With Stricter Gun Laws Really Have Less Crime or Fewer Homicides?
• Is There a Correlation Between Happiness and Carbon Emissions?
• American Foundation for Suicide Prevention. "Suicide statistics." (Feb. 21, 2022) https://afsp.org/suicide-statistics/
• Arthur, Lisa. "What is Big Data?" Forbes. Aug. 15, 2013. (Dec. 18, 2013) http://www.forbes.com/sites/lisaarthur/2013/08/15/what-is-big-data/
• Bansal, Ayush. "Everything you want to know about The Super Bowl Indicator." TechStory. Feb. 14, 2022. (Feb. 16, 2022)
• Barnett, Adrian, et al. "Regression to the mean: what it is and how to deal with it." International Journal of Epidemiology. Aug. 27, 2004. (Feb. 16, 2022)
• Brown, Andrew W., Michelle M, Bohan Brown and David B. Allison. "Belief Beyond the Evidence: Using the Proposed Effect of Breakfast on Obesity to Show 2 Practices that Distort Scientific Evidence." American Journal of Clinical Nutrition. Vol. 98, no. 5. Page 1298. (Dec. 18, 2013) http://ajcn.nutrition.org/content/early/2013/09/04/ajcn.113.064410
• Centers for Disease Control and Prevention. "Measles Cases and Outbreaks." Nov. 19, 2021. (Feb. 21, 2022)
• Centers for Disease Control and Prevention. "Recommended Immunization Schedule for Persons Age 0 through 18 Years: United States, 2013." (Dec. 18, 2013) http://www.cdc.gov/vaccines/schedules/hcp/imz/child-adolescent.html
• Chen, James. "Super Bowl Indicator." Investopedia. Feb. 10, 2022. (Feb. 16, 2022) https://www.investopedia.com/terms/s/superbowlindicator.asp
• Cooper, David, et al. "Twenty-one states raised their minimum wages on New Year's Day." Economic Policy Institute. Jan. 6, 2022. (Feb. 21, 2022) https://www.epi.org/blog/states-minimum-wage-increases-jan-2022/
• ESPN. "Michael Jordan Stats." (Dec. 18, 2013) http://espn.go.com/nba/player/stats/_/id/1035/michael-jordan
• Exact Sports. "Pre-game rituals and superstitions." July 19, 2010. (Feb. 16, 2022) https://exactsports.com/blog/pregame-superstitions/2010/07/19/
• FDA. "FDA Approves First COVID-19 Vaccine." Aug. 23, 2021. (Feb. 21, 2022) https://www.fda.gov/news-events/press-announcements/fda-approves-first-covid-19-vaccine
• Gilovich, Thomas, Robert Vallone and Amos Tversky. "The hot hand in basketball: On the misperception of random sequences." Cognitive Psychology. Vol. 17. Page 295. (Feb. 16, 2022) https://www.sciencedirect.com/science/article/abs/pii/0010028585900106
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Methodology

• Independent vs. Dependent Variables | Definition & Examples

Independent vs. Dependent Variables | Definition & Examples

Published on February 3, 2022 by Pritha Bhandari . Revised on June 22, 2023.

In research, variables are any characteristics that can take on different values, such as height, age, temperature, or test scores.

Researchers often manipulate or measure independent and dependent variables in studies to test cause-and-effect relationships.

• The independent variable is the cause. Its value is independent of other variables in your study.
• The dependent variable is the effect. Its value depends on changes in the independent variable.

Your independent variable is the temperature of the room. You vary the room temperature by making it cooler for half the participants, and warmer for the other half.

Table of contents

What is an independent variable, types of independent variables, what is a dependent variable, identifying independent vs. dependent variables, independent and dependent variables in research, visualizing independent and dependent variables, other interesting articles, frequently asked questions about independent and dependent variables.

An independent variable is the variable you manipulate or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

Independent variables are also called:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation).

These terms are especially used in statistics , where you estimate the extent to which an independent variable change can explain or predict changes in the dependent variable.

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There are two main types of independent variables.

• Experimental independent variables can be directly manipulated by researchers.
• Subject variables cannot be manipulated by researchers, but they can be used to group research subjects categorically.

Experimental variables

In experiments, you manipulate independent variables directly to see how they affect your dependent variable. The independent variable is usually applied at different levels to see how the outcomes differ.

You can apply just two levels in order to find out if an independent variable has an effect at all.

You can also apply multiple levels to find out how the independent variable affects the dependent variable.

You have three independent variable levels, and each group gets a different level of treatment.

You randomly assign your patients to one of the three groups:

• A low-dose experimental group
• A high-dose experimental group
• A placebo group (to research a possible placebo effect )

A true experiment requires you to randomly assign different levels of an independent variable to your participants.

Random assignment helps you control participant characteristics, so that they don’t affect your experimental results. This helps you to have confidence that your dependent variable results come solely from the independent variable manipulation.

Subject variables

Subject variables are characteristics that vary across participants, and they can’t be manipulated by researchers. For example, gender identity, ethnicity, race, income, and education are all important subject variables that social researchers treat as independent variables.

It’s not possible to randomly assign these to participants, since these are characteristics of already existing groups. Instead, you can create a research design where you compare the outcomes of groups of participants with characteristics. This is a quasi-experimental design because there’s no random assignment. Note that any research methods that use non-random assignment are at risk for research biases like selection bias and sampling bias .

Your independent variable is a subject variable, namely the gender identity of the participants. You have three groups: men, women and other.

Your dependent variable is the brain activity response to hearing infant cries. You record brain activity with fMRI scans when participants hear infant cries without their awareness.

A dependent variable is the variable that changes as a result of the independent variable manipulation. It’s the outcome you’re interested in measuring, and it “depends” on your independent variable.

In statistics , dependent variables are also called:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

The dependent variable is what you record after you’ve manipulated the independent variable. You use this measurement data to check whether and to what extent your independent variable influences the dependent variable by conducting statistical analyses.

Based on your findings, you can estimate the degree to which your independent variable variation drives changes in your dependent variable. You can also predict how much your dependent variable will change as a result of variation in the independent variable.

Distinguishing between independent and dependent variables can be tricky when designing a complex study or reading an academic research paper .

A dependent variable from one study can be the independent variable in another study, so it’s important to pay attention to research design .

Here are some tips for identifying each variable type.

Recognizing independent variables

Use this list of questions to check whether you’re dealing with an independent variable:

• Is the variable manipulated, controlled, or used as a subject grouping method by the researcher?
• Does this variable come before the other variable in time?
• Is the researcher trying to understand whether or how this variable affects another variable?

Recognizing dependent variables

Check whether you’re dealing with a dependent variable:

• Is this variable measured as an outcome of the study?
• Is this variable dependent on another variable in the study?
• Does this variable get measured only after other variables are altered?

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Independent and dependent variables are generally used in experimental and quasi-experimental research.

Here are some examples of research questions and corresponding independent and dependent variables.

For experimental data, you analyze your results by generating descriptive statistics and visualizing your findings. Then, you select an appropriate statistical test to test your hypothesis .

The type of test is determined by:

• your variable types
• level of measurement
• number of independent variable levels.

You’ll often use t tests or ANOVAs to analyze your data and answer your research questions.

In quantitative research , it’s good practice to use charts or graphs to visualize the results of studies. Generally, the independent variable goes on the x -axis (horizontal) and the dependent variable on the y -axis (vertical).

The type of visualization you use depends on the variable types in your research questions:

• A bar chart is ideal when you have a categorical independent variable.
• A scatter plot or line graph is best when your independent and dependent variables are both quantitative.

To inspect your data, you place your independent variable of treatment level on the x -axis and the dependent variable of blood pressure on the y -axis.

You plot bars for each treatment group before and after the treatment to show the difference in blood pressure.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.

In statistics, dependent variables are also called:

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .

• The type of soda – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

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Independent and Dependent Variables Examples

The independent and dependent variables are key to any scientific experiment, but how do you tell them apart? Here are the definitions of independent and dependent variables, examples of each type, and tips for telling them apart and graphing them.

Independent Variable

The independent variable is the factor the researcher changes or controls in an experiment. It is called independent because it does not depend on any other variable. The independent variable may be called the “controlled variable” because it is the one that is changed or controlled. This is different from the “ control variable ,” which is variable that is held constant so it won’t influence the outcome of the experiment.

Dependent Variable

The dependent variable is the factor that changes in response to the independent variable. It is the variable that you measure in an experiment. The dependent variable may be called the “responding variable.”

Examples of Independent and Dependent Variables

Here are several examples of independent and dependent variables in experiments:

• In a study to determine whether how long a student sleeps affects test scores, the independent variable is the length of time spent sleeping while the dependent variable is the test score.
• You want to know which brand of fertilizer is best for your plants. The brand of fertilizer is the independent variable. The health of the plants (height, amount and size of flowers and fruit, color) is the dependent variable.
• You want to compare brands of paper towels, to see which holds the most liquid. The independent variable is the brand of paper towel. The dependent variable is the volume of liquid absorbed by the paper towel.
• You suspect the amount of television a person watches is related to their age. Age is the independent variable. How many minutes or hours of television a person watches is the dependent variable.
• You think rising sea temperatures might affect the amount of algae in the water. The water temperature is the independent variable. The mass of algae is the dependent variable.
• In an experiment to determine how far people can see into the infrared part of the spectrum, the wavelength of light is the independent variable and whether the light is observed is the dependent variable.
• If you want to know whether caffeine affects your appetite, the presence/absence or amount of caffeine is the independent variable. Appetite is the dependent variable.
• You want to know which brand of microwave popcorn pops the best. The brand of popcorn is the independent variable. The number of popped kernels is the dependent variable. Of course, you could also measure the number of unpopped kernels instead.
• You want to determine whether a chemical is essential for rat nutrition, so you design an experiment. The presence/absence of the chemical is the independent variable. The health of the rat (whether it lives and reproduces) is the dependent variable. A follow-up experiment might determine how much of the chemical is needed. Here, the amount of chemical is the independent variable and the rat health is the dependent variable.

How to Tell the Independent and Dependent Variable Apart

If you’re having trouble identifying the independent and dependent variable, here are a few ways to tell them apart. First, remember the dependent variable depends on the independent variable. It helps to write out the variables as an if-then or cause-and-effect sentence that shows the independent variable causes an effect on the dependent variable. If you mix up the variables, the sentence won’t make sense. Example : The amount of eat (independent variable) affects how much you weigh (dependent variable).

This makes sense, but if you write the sentence the other way, you can tell it’s incorrect: Example : How much you weigh affects how much you eat. (Well, it could make sense, but you can see it’s an entirely different experiment.) If-then statements also work: Example : If you change the color of light (independent variable), then it affects plant growth (dependent variable). Switching the variables makes no sense: Example : If plant growth rate changes, then it affects the color of light. Sometimes you don’t control either variable, like when you gather data to see if there is a relationship between two factors. This can make identifying the variables a bit trickier, but establishing a logical cause and effect relationship helps: Example : If you increase age (independent variable), then average salary increases (dependent variable). If you switch them, the statement doesn’t make sense: Example : If you increase salary, then age increases.

How to Graph Independent and Dependent Variables

Plot or graph independent and dependent variables using the standard method. The independent variable is the x-axis, while the dependent variable is the y-axis. Remember the acronym DRY MIX to keep the variables straight: D = Dependent variable R = Responding variable/ Y = Graph on the y-axis or vertical axis M = Manipulated variable I = Independent variable X = Graph on the x-axis or horizontal axis

• Babbie, Earl R. (2009). The Practice of Social Research (12th ed.) Wadsworth Publishing. ISBN 0-495-59841-0.
• di Francia, G. Toraldo (1981). The Investigation of the Physical World . Cambridge University Press. ISBN 978-0-521-29925-1.
• Gauch, Hugh G. Jr. (2003). Scientific Method in Practice . Cambridge University Press. ISBN 978-0-521-01708-4.
• Popper, Karl R. (2003). Conjectures and Refutations: The Growth of Scientific Knowledge . Routledge. ISBN 0-415-28594-1.

Related Posts

Cause and Effect

Cause and effect generator.

In every aspect of life, actions lead to outcomes. Understanding cause and effect is crucial for making informed decisions, predicting consequences, and solving problems effectively. This principle underpins scientific discoveries, historical events, and daily interactions. By recognizing the connections between actions and their impacts, we gain insights into the world around us and improve our ability to navigate complex situations. In this article, we will explore the fundamental nature of cause and effect, illustrating its importance through various examples and highlighting its role in critical thinking and decision-making processes.

What is Cause and Effect?

Cause and effect is a concept used to describe the relationship between events or things, where one is the result of the other or others. This concept is fundamental in various fields, including science, philosophy, and everyday reasoning. Here’s a detailed definition and meaning:

• Cause: The reason something happens; an event or action that leads to a certain outcome.
• Effect: The result or outcome that occurs due to a specific cause.

The relationship between cause and effect implies that certain events (causes) directly bring about other events (effects). Understanding this relationship helps in explaining why things happen and predicting what might happen next based on current actions or events.

Historical Context of Cause and Effect

The exploration of cause and effect can be traced back to ancient philosophies. Aristotle, for instance, discussed the principles of causality in his works, emphasizing the importance of understanding causes to comprehend the world around us.

Cause and Effect Examples

In Sentence

• Heavy rain causes flooding in the streets .
• Studying hard for a test leads to getting a high score .
• Eating too much junk food results in gaining weight .
• Exercising regularly improves physical fitness .
• Forgetting to water the plants causes plants to wilt .
• Leaving a candle unattended can start a fire .
• Not wearing sunscreen on a sunny day results in getting sunburned .
• Saving money every month leads to building a substantial savings account .
• Breaking a bone requires needing a cast to heal .
• Global warming leads to melting polar ice caps .
• Not getting enough sleep results in feeling tired and irritable .
• Overfishing in the oceans causes a decrease in fish populations .
• Practicing a musical instrument daily leads to becoming proficient at playing it .
• Not paying attention while driving can result in a car accident .
• Smoking cigarettes leads to developing lung disease .
• Spending too much time on social media causes decreased productivity .
• Implementing green energy solutions results in a reduction in carbon footprint .
• Conflict between countries can lead to war or heightened political tensions .
• Cutting down forests results in loss of wildlife habitat .
• High levels of stress cause health problems such as high blood pressure and anxiety .

For Students

• Not completing homework can lead to lower grades .
• Attending all classes results in better understanding of the material .
• Studying in groups can help with improving problem-solving skills .
• Using a planner helps in managing time effectively .
• Participating in class discussions can boost confidence .
• Sleeping well the night before ensures alertness during the exam .
• Reading additional resources can enhance knowledge .
• Turning in assignments on time leads to better grades .
• Taking notes during lectures aids in better retention of information .
• Seeking help when needed prevents falling behind in studies .

In Research

• Increasing sample size often results in more accurate data .
• Applying a new technique can yield different results compared to traditional methods.
• Proper citation of sources prevents plagiarism .
• Using control groups ensures validity of the experiment .
• Consistent methodology leads to reproducible results .
• Analyzing data carefully avoids misinterpretation .
• Formulating a clear hypothesis guides the research direction .
• Reviewing literature extensively provides context for the study .
• Collaborating with peers can enhance research quality .
• Adhering to ethical guidelines maintains research integrity .
• If you touch a hot stove , you will burn your hand .
• Eating too much candy can cause a stomach ache .
• Sharing your toys will make your friends happy .
• Brushing your teeth prevents cavities .
• Doing your chores can earn you an allowance .
• If you run in the house , you might break something .
• Being kind to others makes you a good friend .
• Going to bed late means feeling sleepy the next day .
• Playing outside helps you stay healthy .
• Listening to your parents keeps you safe .

For Grade 3

• Reading every day helps you learn new words .
• If you water the plants , they will grow .
• Eating healthy food gives you more energy .
• Paying attention in class helps you understand the lesson .
• Playing outside makes you feel happy .
• Helping your classmates makes you a good friend .
• Finishing your homework on time makes your teacher proud .
• Wearing a coat in winter keeps you warm .
• If you clean your room , it will look nice .
• If you follow the rules , you will stay out of trouble .

In Real Life

• Driving too fast can cause car accidents .
• Not saving money may result in financial problems .
• Getting regular exercise improves overall health .
• Not getting enough sleep leads to feeling tired .
• Using public transportation reduces traffic congestion .
• Eating a balanced diet contributes to better health .
• Not paying bills on time results in late fees .
• Being on time for work shows professionalism .
• Using a budget helps in managing finances .
• Recycling helps protect the environment .

Types of Causes

• Immediate Causes : Directly responsible for an effect.
• Contributory Causes : Factors that play a role in creating an effect but are not solely responsible.
• Necessary and Sufficient Causes : Necessary causes must be present for an effect to occur, while sufficient causes alone can produce the effect.

Importance of Cause and Effect in Decision Making

Understanding cause and effect is crucial for making informed decisions. By anticipating the outcomes of actions, individuals and organizations can plan more effectively and avoid unintended consequences.

Signal Words in Cause and Effect Sentence

Signal words in cause and effect sentences help indicate the relationship between actions or events. They clearly show that one event is the result of another. Here are some common signal words and phrases used in cause and effect sentences:

Cause Signal Words

These words indicate the cause or reason something happens.

• As a result of
• On account of
• Because it rained, the event was canceled.
• Due to heavy traffic, she was late to the meeting.

Effect Signal Words

These words indicate the effect or outcome of a cause.

• Consequently
• As a result
• For this reason
• She studied hard; therefore, she passed the exam.
• The power went out; as a result, we had to use candles.

Combined Signal Words

These words or phrases can indicate both cause and effect in a sentence.

• If it rains, then the game will be postponed.
• She was so tired that she fell asleep immediately.

Sample Sentences

• Because it was raining, we stayed inside.
• The roads were icy; consequently , there were many accidents.
• Due to the high demand, prices increased.
• He didn’t study; therefore , he didn’t pass the exam.
• Since the store was closed, we couldn’t buy groceries.

Activities to Learn About Cause and Effect

For young children.

• Simple Experiments : Use water, food coloring, and paper towels to show how colors mix. Explain how one action (adding color) causes a change (new color formation).
• Storytime Discussions : Read stories and discuss the events that lead to outcomes. Ask questions like, “What happened when the character did this?”
• Domino Effect : Set up a line of dominos and show how knocking one down causes a chain reaction. Let them experiment with different setups.
• Cooking Activities : Baking cookies can illustrate how mixing ingredients and applying heat (the cause) results in baked cookies (the effect).

For Older Children

• Science Experiments : Conduct experiments where they can change one variable at a time and observe the outcomes, like growing plants under different conditions.
• Interactive Games : Use games that require problem-solving and critical thinking, such as “The Incredible Machine” or “Minecraft”.
• Chain Reactions : Create Rube Goldberg machines. These are complex devices that perform simple tasks through a series of cause-and-effect steps.
• Historical Events : Study historical events and discuss how one event led to another. This helps in understanding broader cause-and-effect relationships.
• Physics Projects : Building simple machines or engaging in robotics projects can illustrate cause and effect in a hands-on manner.
• Programming : Learning to code can show direct cause and effect as students write commands (causes) and see the results (effects) immediately.
• Debates and Discussions : Analyze current events or historical scenarios, debating how certain actions led to specific outcomes.
• Environmental Studies : Investigate how human actions impact the environment, such as pollution leading to climate change.
• Case Studies : Examine case studies in various fields (business, medicine, engineering) to understand cause-and-effect relationships.
• Simulations and Models : Use computer simulations to see how changing variables affects outcomes in systems like economics or weather patterns.
• Critical Analysis : Write essays or reports analyzing cause-and-effect relationships in literature, film, or real-world events.
• Project Management : Implement project management techniques to see how planning and execution impact project outcomes.

General Activities

• Mind Mapping : Create mind maps that outline causes and their potential effects.
• If-Then Scenarios : Play “if-then” games, predicting what will happen if certain actions are taken.
• Reflective Journals : Keep journals where you reflect on daily actions and their outcomes to see patterns over time.
• Experiential Learning : Engage in hands-on learning activities, where the process and results are directly linked, such as gardening, carpentry, or art projects.

Cause and Effect Questions and Answers

What is the definition of cause and effect?

Cause and effect describe the relationship where one event (cause) makes another event happen (effect).

Why is understanding cause and effect important?

Understanding cause and effect helps identify reasons behind occurrences, allowing for better decision-making and problem-solving.

How can cause and effect be identified?

Identify cause and effect by looking for events that trigger other events, using words like “because,” “therefore,” and “as a result.”

What are examples of cause and effect in everyday life?

Examples include eating healthy (cause) leading to better health (effect) or studying (cause) resulting in good grades (effect).

How do cause and effect impact science?

In science, cause and effect help explain natural phenomena and develop theories by identifying the reasons behind events.

Can cause and effect be used in writing?

Yes, writers use cause and effect to structure arguments, explain reasons behind actions, and enhance narratives.

What is a cause and effect diagram?

A cause and effect diagram, also known as a fishbone diagram, visually maps out the causes of a specific event.

How do you teach cause and effect to children?

Teach cause and effect to children through simple examples, storytelling, and hands-on activities that show direct relationships.

What is the difference between cause and correlation?

Cause directly leads to an effect, while correlation indicates a relationship between two variables without proving one causes the other.

How does cause and effect relate to critical thinking?

Cause and effect analysis enhances critical thinking by encouraging examination of reasons behind events and their outcomes.

Text prompt

• Instructive
• Professional

Write about the Cause and Effect of global warming on polar ice caps.

Create an essay discussing the Cause and Effect of daily exercise on mental health.

• Research article
• Open access
• Published: 12 September 2024

Genetically predicted effects of 10 sleep phenotypes on revision of knee arthroplasty: a mendelian randomization study

• Zhiguo Bi 1 ,
• Yimeng Cai 2 ,
• Jintian Chen 1 ,
• Xiaotong Shi 1 ,
• Shiyu Liao 1 , 3 ,
• Long Jin 1 , 4 &
• Jianguo Liu 1  

Journal of Orthopaedic Surgery and Research volume  19 , Article number:  563 ( 2024 ) Cite this article

Metrics details

Accumulating evidence has suggested that sleep disturbances and disorders are common in patients who undergo knee arthroplasty. Revision surgery represents one of the most catastrophic outcomes of knee arthroplasty. However, it remains unclear whether sleep traits are the causes or consequences of knee arthroplasty revision. This study aimed to genetically examine the relationships between sleep traits and knee arthroplasty revision.

To determine the causal relationship between sleep traits and knee arthroplasty revision, we employed two-sample Mendelian randomization (MR) using summary statistics from the largest publicly available genome-wide association studies (GWASs). The MR design uses genetic variants as instrumental variables to help separate causal relationships from non-causal associations. The main analyses included an inverse variance weighted (IVW) meta-analysis to obtain primary effect estimates. Sensitivity analyses involving the weighted median approach and MR-Egger regression were also conducted to check for potential pleiotropic biases. Numerous complementary sensitivity analyses were also performed to identify statistically significant causal correlations when there were horizontal pleiotropy and heterogeneity across variants. Finally, a reverse MR analysis was performed to evaluate the possibility of reverse causation.

In the absence of heterogeneity and horizontal pleiotropy, the IVW method revealed that genetically-predicted short sleep duration short sleep duration (average sleep duration of 24 h is 6 h or less) was positively correlated with the risk of knee arthroplasty revision (odds ratio = 1.03, 95% confidence interval = 1.01–1.05, and P  = 0.003), while the association between genetically-predicted long sleep duration and knee arthroplasty was negative. The reverse MR analysis did not yield evidence supporting reverse causality relation between knee arthroplasty revision and sleep phenotypes.

This research indicated that, of the 10 sleep phenotypes we analyzed, only sleep duration was causally associated with knee arthroplasty revision. These discoveries added to the understanding of the role of sleep traits in the etiology of knee arthroplasty revision, which might further expand our insights into the prevention of knee arthroplasty revision.

1. Genetically-predicted short sleep duration increases risk of knee arthroplasty revision.

2. Mendelian randomization used to analyze sleep traits and knee revision relationship.

3. Sleep duration causally linked to lower risk of knee arthroplasty revision.

4. No evidence found for reverse causality between knee revision and sleep traits.

5. Study enhances understanding of sleep’s role in knee arthroplasty outcomes.

Introduction

Osteoarthritis (OA) is a degenerative joint disease that affects multiple joint tissues, including cartilage, subchondral bone, the infrapatellar fat pad, the meniscus, and the synovial membrane [ 1 ]. It is the most common joint disorder globally, with knee osteoarthritis being particularly prevalent, affecting approximately 16% of the global population [ 2 ]. The risk factors for OA can be broadly categorized into two groups: person-level factors (such as age, gender, obesity, genetics, and diet) and joint-level factors (including injury, malalignment, and abnormal joint loading) [ 3 ]. These factors interact in a complex manner to contribute to the onset and progression of the disease. Total knee arthroplasty (TKA) has emerged as the most efficacious and cost-effective intervention for managing advanced-stage knee arthritis. Despite the success of TKA, the escalating incidence of revision surgeries remains a formidable challenge, which not only threatens patients’ health but also poses substantial pressure on healthcare systems [ 4 ]. Revision surgeries, necessitated by multiple factors, ranging from infection and aseptic loosening to periprosthetic fractures, subjecting patients to some significant risks including complications [ 5 , 6 ]. Moreover, some of these surgeries occur under ambiguous circumstances—from minor technical glitches to psychological distress—highlighting a complicated interplay among various determinants of outcomes [ 7 ].

The intricate relationships among lifestyle, mental well-being, and physical health have been extensively acknowledged and might involve energy balance, metabolism [ 8 ], and patient engagement in rehabilitation and coping mechanisms [ 9 ]. These factors are crucial for promoting physical and functional recovery after TKA [ 10 , 11 , 12 ]. Sleep, a critical component of lifestyle and health, has drawn attention for its potential impact on the risk of revision total knee arthroplasty (rTKA) [ 13 , 14 , 15 ]. Evidence suggests that sleep duration and quality before and after joint replacement surgeries can significantly affect pain management, opioid use, and postoperative recovery [ 16 , 17 , 18 ]. For instance, improved sleep duration has been linked to lower postoperative pain levels [ 16 ]. Preoperative sleep quality was also strongly correlated with postoperative functionality, discomfort levels, and analgesic usage [ 18 ]. Nonetheless, the literature presents conflicting findings on the relationship between sleep and TKA outcomes. Some studies reported no direct association between preoperative sleep quality and postoperative pain [ 19 , 20 ], while others, such as a study by Luo et al., found a significant influence of preoperative sleep characteristics on clinical outcome and postoperative length of hospital stay [ 18 ]. Sleep phenotypes have been proposed as potential risk factors for postoperative morbidity following primary joint arthroplasty. For instance, patients with sleep apnea have a higher rate of rTKA after undergoing TKA compared to those without sleep apnea [ 13 , 21 , 22 ],. Improved sleep quality and duration have been linked to better postoperative outcomes and higher patient satisfaction following TKA [ 23 ]. Enhanced sleep quality is also associated with lower pain levels and greater range of motion after surgery [ 24 ]. Moreover, preoperative sleep disorders have been identified as independent risk factors for chronic postsurgical pain following TKA [ 25 ]. Decreased functional capability can adversely affect sleep quality after TKA [ 26 ], and poor sleep quality has been correlated with worse patient-reported outcomes regarding function and pain [ 27 ]. Additionally, suboptimal sleep may impair immune function, increasing the risk of postoperative infection [ 28 , 29 ]. Consequently, sleep disturbances may play a role in the need for rTKA.

Given these contradicting findings and the substantial impact of revision surgeries on patients’ well-being and healthcare resources, this study aimed to conduct an in-depth investigation into this issue. We sought to clarify the causal relationship between sleep-related phenotypes and the likelihood of TKA patients undergoing revision surgery. By addressing this gap in the literature, our research endeavored to contribute to more comprehensive preoperative assessment and intervention strategies, with an attempt to mitigate the risk of revision surgeries and improve the outcomes of patients receiving TKA.

Mendelian randomization represents a methodological advancement capable of addressing the challenges of unmeasured confounding and reverse causation inherent to traditional observational epidemiological studies [ 30 ]. This approach facilitates causal inference by employing genetic variants as proxies for modifiable risk factors or health outcomes [ 31 ]. Specifically, MR leverages genetic data, such as single nucleotide polymorphisms (SNPs) [ 32 ], which are associated with an exposure (e.g., sleep phenotype), and uses these as instrumental variables. This methodology allows for the assessment of the causal impact of the exposure on an outcome of interest (in this instance, revision total knee arthroplasty). One of the principal advantages of MR is its utilization of genetic variants that are randomly allocated at conception. This inherent randomness enables the simulation of a randomization process, thereby offering a quasiexperimental design that mitigates the biases associated with observational studies [ 33 ]. These genetic variants are not influenced by behavioral or environmental factors throughout the lifetime of an individual, thereby significantly reducing susceptibility to bias from reverse causation and the impact of transient fluctuations in exposure. Consequently, the effects observed in MR studies can be interpreted as reflecting lifetime exposure differences, enhancing the validity of the causal inferences drawn [ 34 ]. To our knowledge, no study prior to this study has elucidated the causal effect of sleep traits on revision after knee arthroplasty. In the present study, we conducted a Mendelian randomization analysis, drawing upon summary statistics from extensive genome-wide association studies (GWASs). Our objective was to investigate the causal relationship between sleep traits and the necessity for revision total knee arthroplasty. Furthermore, a sensitivity analysis was meticulously conducted, aiming to evaluate the influence of our hypotheses on the study findings and to verify the robustness and reliability of the results obtained.

Materials and methods

Data sources, sleep-related phenotypes.

By searching PubMed for published genome-wide association studies (GWASs), a total of 10 sleep-related traits were ascertained, including sleep duration, short sleep duration, long sleep duration [ 35 ], daytime napping [ 36 ], insomnia [ 37 ], chronotype [ 38 ], daytime sleepiness [ 39 ], snoring [ 40 ], sleep apnea [ 41 ], and trouble falling asleep [ 40 ]. The data sources for each sleep trait are summarized in Table  1 . The genetic variant relationships for all the sleep phenotypes were evaluated in participants with European ancestry, and all GWAS data were adjusted for sex, age, and study-specific variables.

Revision of knee arthroplasty

The GWAS summary statistics of rTKA were obtained from the MRC-IEU consortium, which included 5,657 cases and 457,276 controls of European ancestry. Moreover, GWASs were adjusted for age, sex, and several principal components. A total of 9.85 million variants were retrieved.

Data availability

The data used in the MR analysis were publicly available and did not require specific ethical approval. The summary GWAS data of the sleep-related traits are available at The NHGRI-EBI GWAS Catalog (available at: https://www.ebi.ac.uk/gwas/home , accessed date: 1 February 2024) and The Sleep Disorder Knowledge Portal (SDKP, RRID: SCR_016611, available at: http://sleepdisordergenetics.org/ , accessed date: 1 February 2024). The GWAS Catalog represents a comprehensive, publicly accessible resource that aggregates and curates all published genome-wide association studies (GWAS) and their associated findings. This catalog is the result of a collaborative effort between the National Human Genome Research Institute (NHGRI) and the European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI). Since the inception of the first GWAS on age-related macular degeneration in 2005, the catalog has consistently included all eligible studies, summarizing an extensive array of heterogeneous and unstructured data from the scientific literature into a coherent, meticulously curated, and quality-controlled repository [ 42 ]. The data contained within the catalog serve as a foundational resource for a wide range of researchers, including biologists, bioinformaticians, and clinical/translational scientists. This study provided a critical starting point for subsequent exploratory initiatives aimed at identifying causal genetic variants, elucidating the underlying mechanisms of diseases, and pinpointing potential targets for the development of novel therapeutic interventions. To be considered for inclusion within the GWAS Catalog, both studies and their reported associations are subject to rigorous selection criteria. Specifically, studies must be based on array technologies and analyze more than 100,000 single nucleotide polymorphisms across the genome. Moreover, SNP-trait associations are required to demonstrate statistical significance, with a P value threshold set at less than 1 × 10 − 5 . This stringent inclusion criterion ensures that the catalog remains a high-quality, reliable resource for the scientific community, facilitating advanced research and discovery in the field of genomics [ 43 ]. The Sleep Disorder Knowledge Portal constitutes an integral component of the Common Metabolic Diseases Knowledge Portal (CMDKP). The CMDKP consolidates and examines an extensive array of genetic association findings, epigenomic annotations, and outputs from computational prediction methodologies. This comprehensive aggregation facilitates the provision of data, visualizations, and analytical tools within an open-access framework. The development and maintenance of the CMDKP are underpinned by funding from the Accelerating Medicines Partnership. A collaborative effort spearheads this initiative, comprising a dedicated team of scientists and software engineers affiliated with the Broad Institute, the University of Michigan, and the University of Oxford. Furthermore, the project benefits from the contributions of numerous other partners spanning academic institutions, the industrial sector, and non-profit organizations globally. This collaborative approach ensures that the CMDKP population remains a pivotal resource for advancing the understanding of sleep disorders within the broader context of common metabolic diseases [ 35 ].

The summary statistics concerning the knee arthroplasty revision GWAS are available at the IEU Open GWAS (available at: https://gwas.mrcieu.ac.uk , accessed date: 1 February 2024). The IEU Open GWAS database represents a cutting-edge, open-source, open-access data infrastructure designed with scalability and high performance in mind and is hosted on a cloud-based platform. Its primary function is to import and disseminate comprehensive GWAS summary datasets along with their corresponding metadata to facilitate research within the scientific communities [ 44 ]. As of the current update, it houses approximately 126 billion genetic associations derived from 14,582 complete GWAS datasets. These datasets span a diverse array of human phenotypes and disease outcomes, encompassing a variety of populations. This expansive and meticulously curated collection serves as a valuable resource for researchers aiming to advance our understanding of genetic underpinnings across a broad spectrum of human conditions [ 45 ].

Selection of instrumental variables (IVs)

In MR analyses, valid instrumental variables are characterized by three key assumptions (Fig.  1 ): relevance, independence, and exclusion restriction. The relevance assumption states that instrumental variables are associated with the risk factor of interest. The independence assumption expounds that instrumental variables do not share any common causes with the outcome. Finally, the exclusion restriction assumption dictates that instrumental variables affect the outcome only indirectly through the risk factor and not directly through the outcome itself [ 46 ].

Flowchart of the MR study design

Mendelian Randomization (MR) uses genetic variants (SNPs) as proxies for modifiable risk factors to assess causal relationships between exposures (such as sleep traits) and outcomes (such as knee arthroplasty revision). We sourced genetic data from large GWAS databases for sleep traits and knee arthroplasty revision, selecting SNPs significantly associated with sleep traits ( P  < 5 × 10 − 8 ) and ensuring independence through clumping (R 2  < 0.001). After aligning effect sizes and alleles across datasets, we excluded weak instruments and potential confounders using F-statistics and PhenoScanner. The primary MR analysis was conducted using the Inverse Variance Weighted (IVW) method, with additional sensitivity analyses (MR-Egger, weighted median, MR-PRESSO) to check for pleiotropy and other biases. Statistical power calculations were performed to ensure robustness. Our results indicated a causal relationship between sleep traits and knee arthroplasty revision, with short sleep duration increasing the risk and long sleep duration decreasing it, confirmed through rigorous sensitivity checks.

To select valid instrumental variables, we employed several quality control procedures. We began by identifying single nucleotide polymorphisms (SNPs) related to exposure at the genome-wide significance threshold ( P  < 5 × 10 − 8 for sleep traits and P  < 5 × 10 − 6 for revision of knee arthroplasty) from the initial GWAS. Then, we applied the Plink clumping procedure with R 2  < 0.001 and a window size of 10,000 kb to ensure that the selected SNPs were independent and free from linkage disequilibrium (LD). To minimize weak instrument bias, we employed F-statistics > 10 for all exposures. We also eliminated palindromic SNPs to prevent distortion of strand orientation or allele coding. Finally, we addressed potential pleiotropy by removing SNPs via the MR pleiotropy residual sum and outlier (MR-PRESSO) method. The details are provided in the Supplementary Tables (Supplementary Information).

Statistical analysis

The inverse variance weighted (IVW) method was utilized for weighting the influence of each SNP on exposure and outcome data as the best-powered unbiased estimation of determining causality if there was no horizontal pleiotropy and if all IVs were valid [ 47 ]. When pleiotropy existed, the MR‒Egger, weighted median, and weighted mode approaches were also used to obtain more reliable and robust estimates. If there was heterogeneity among the SNPs included in each analysis, random effects IVW was performed.

We utilized the PhenoScanner database (available at: http://www.phenoscanner.medschl.cam.ac.uk , accessed date: 1 February 2024) to explore potential associations of the chosen SNPs with confounding traits. After excluding these SNPs, we re-performed the analysis [ 48 ]. R 2 and F-statistics were calculated to explore weak IV bias using a previously reported method [ 49 ]. R 2 is the fraction of variation explained by IVs in exposure factors, and an F-statistic < 10 indicates that the weak IV bias will be abolished.

Sensitivity analyses

To evaluate the sensitivity of genetic causal effects, we conducted a series of sensitivity analyses by using MR-Egger regressions and weighted median methods. In addition, we estimated directional pleiotropy via the MR‒Egger intercept and evaluated different genetic variants by adopting Cochran’s Q statistic to assess heterogeneity among IVW estimates [ 50 ]. Furthermore, we employed MR-PRESSO to evaluate and adjust potential outlier SNPs [ 51 ]. The leave-one-out sensitivity method was employed to ascertain whether a particular genetic locus had an effect on random estimates. To further highlight the sensitivity of the findings, scatterplots, forest plots, and funnel plots were generated.

Statistical power and bias and type 1 error rate

The post statistical power was calculated using an online tool (available at: https://sb452.shinyapps.io/power , accessed date: 1 February 2024). We carried out the analyses of Bias and Type 1 Error Rate for Mendelian Randomization with Sample Overlap to evaluate the potential bias caused by sample overlap using a website-based tool (available at: https://sb452.shinyapps.io/overlap/ , accessed date: 1 February 2024) [ 52 ].

We set the threshold for statistical significance at P  < 0.005 (Bonferroni corrected P -value correcting for 10 exposures and 1 outcome) [ 53 , 54 ]. Additionally, a bidirectional MR test was carried out. A P value between 0.005 and 0.05 was considered to indicate evidence of a potential relationship. The “TwoSampleMR” and “MRPRESSO” packages in R, along with R version 4.3.0 (R Foundation for Statistical Computing, Vienna, Austria), were used for all the statistical analyses.

The complete MR analysis information is presented in the Supplementary Tables, including the characteristics of the selected SNPs for each sleep trait, the outcomes of the heterogeneity tests, and the results of the causal effect estimates.

Table S1 summarizes sleep-associated SNPs from GWAS. Table S2 compares causal effects of sleep phenotypes on knee revision. Table S3 assesses causal effects of knee revision on sleep phenotypes. Table S4 outlines SNPs relating to knee arthroplasty revision. Table S5 examines causal relationships between sleep factors and hip arthroplasty.

Causality between sleep traits and knee arthroplasty revision

As depicted in Fig.  2 , there was compelling evidence of a causal relationship between sleep duration and rTKA according to the IVW method (odds ratio (OR) = 0.99, 95% confidence interval (95%CI): 0.98–0.99, and P  = 0.00004) and weighted median method (OR = 0.99, 95%CI: 0.98–0.99, and P  = 0.0007). For 62 instruments, MRPRESSO detected no potential outliers. In addition, a causal relationship was found between short sleep duration (average sleep duration of 24 h is 6 h or less) and knee arthroplasty revision according to the IVW method (OR = 1.03, 95%CI = 1.01–1.05, and P  = 0.003). For 22 IVs, there was no potential outlier according to the MR-PRESSO. Furthermore, there was no significant heterogeneity or pleiotropy among the instrumental variable effects. A diagram of the above MR analysis results for the scatter plot of SNP effects, leave-one-way analysis, forest plot, and funnel plot are displayed in Figs.  3 and 4 . However, the results of MR analysis revealed no causal relationships between the other sleep traits and revision after knee arthroplasty.

Mendelian randomization estimation for causality of sleep traits on revision of knee arthroplasty

Scatterplots of sleep duration based on SNP effect ( a ), one-way analysis ( b ), forest plot (c), and funnel plot (d)

Scatterplots of SNP effects based on sleep duration ( a ), one-way analysis ( b ), forest plots (c), and funnel plots (d)

Mendelian randomization estimation for causality between revision of knee arthroplasty and sleep traits

Causality between knee arthroplasty revision and sleep traits

We investigated potential reverse causal relationships between sleep traits and revision of knee arthroplasty using two-sample reverse MR. Specifically, we employed the IVW, MR‒Egger, weighted median (WME), weighted mode, and simple mode methods to analyze independent genetic associations. As shown in Fig.  5 and Supplementary Table S2, the IVW analysis revealed no statistically significant evidence of reverse causation. Consistent with this, the MR‒Egger, WME, weighted mode, and simple mode approaches did not demonstrate any significant reverse relationships between genetic predictors of revision of knee arthroplasty and sleep traits either.

Principal findings

To the best of our knowledge, this was the first study to use various MR methods to explore the bidirectional causal link between sleep traits and revision after knee arthroplasty. In this study, we employed the largest GWAS summary-level dataset available to date from various sources to perform a two-sample MR analysis to fully investigate the causative effect of sleep traits on revision after knee arthroplasty. The MR analysis revealed a role of sleep duration in decreasing the risk of knee arthroplasty revision and a promoting role of short sleep duration in knee arthroplasty revision. In contrast, there was no causal relationship between the other eight sleep phenotypes and knee arthroplasty revision. In addition, no causal relationship was detected between sleep traits and other arthroplasty surgeries.

Comparison with other studies

The associations between sleep traits and the outcome of knee arthroplasty were inconsistent across the reported studies. Furthermore, prior studies did not directly examine the relationship between sleep traits and risk for knee arthroplasty revision using MR analysis. In view of this, our MR analysis was novel for exploring the potential causal role of sleep characteristics in knee arthroplasty revision. For instance, observational research has suggested that bodily function and pain interference are strongly associated with sleep disturbance and dyspnea after joint arthroplasty, but the causation between sleep disturbance and joint arthroplasty revision has not been explored [ 55 ]. Our IVW and weighted median estimates demonstrated a significant causal association between sleep duration and revision of knee replacement. In a randomized clinical trial involving 148 patients, Gong et al. suggested that sleep time was significantly correlated with the range of motion after TKA [ 24 ]. Conversely, several retrospective investigations concluded that sleep duration did not correlate with any clinical variables and did not appear to be a meaningful metric for measuring TKA outcomes; however, these studies examined outcomes only within the first 90 days after surgery [ 20 ].

A longitudinal TKA study showed a significant relationship between insomnia 2 weeks before TKA and postoperative pain and functional outcomes [ 56 ]. Daytime napping could provide an opportunity to augment night-time sleep, which might promote the recovery of physical and mental performance [ 57 ]. However, our MR analysis did not find any causal correlation between insomnia, daytime napping, chronotype, or knee arthroplasty revision. One potential reason for the inconsistent results in those GWAS data might be the low sensitivity of the self-reported phenotypic data, indicated by categorical variables such as never/sometimes/usually or yes/no. It is important to mention that categorical data are not amenable to quantitative analysis, as it does not allow for numerical or arithmetic operations. Moreover, daytime sleepiness was demonstrated to be uncorrelated with postoperative outcomes and complications [ 58 ], which was consistent with our MR results.

We found no causal role of sleep apnea or snoring in the revision of knee arthroplasty. This finding was contrary to that of a previous study, which indicated that joint arthroplasty patients with sleep apnea had greater odds of revision surgery [ 14 ]. However, this study was retrospective and included data on the rate of joint revision over a two-year period alone. We hypothesized that the discrepancy between the current MR results and observational findings might derive, in part, from limitations of observational research, follow-up time, and different ancestral bloodlines. Moreover, patients with sleep apnea were more likely to experience pulmonary complications after TJA [ 59 ], which was contrary to the findings reported by Shen et al. and Thompson et al. They found the rate of postoperative cardiopulmonary problems was low in patients who underwent joint arthroplasty and the patients had a high risk of sleep apnea [ 60 ]. However, the causal relationship between joint arthroplasty and sleep apnea remains unclear. Accordingly, prospective studies with longer-term data are necessary to investigate the association between sleep apnea and outcomes following arthroplasty.

Our reverse MR analysis exhibited that knee arthroplasty revision did not exert a notable effect on sleep traits, which was partially consistent with the findings of an observational cohort study in which sleep amount and timing parameters remained steady throughout hospital stay [ 61 ]. Additionally, a prospective study indicated that TKA patients had poor sleep quality both preoperatively and postoperatively after excluding patients with a history of sleep disturbance. The daily use of exogenous melatonin did not have any noteworthy impact on sleep quality [ 62 ]. These findings implied that sleep disturbances observed in prior observational studies before and after surgery might have been transient and related to the surgical procedure itself rather than long-term risk exposure or postoperative outcomes.

Identifying risk factors for revision surgery is challenging due to various factors. First, the high survival rates associated with total knee replacement (TKA) implants at 10 years (95.8–96.6%) and 20 years (89.7%, 87.5–91.5%) made it difficult to gather accurate information on revisions [ 63 ]. Most of the included articles had follow-up periods of less than 10 years. Conducting studies of appropriate duration to obtain precise revision data can thus be both time-consuming and expensive. Multiple factors contributed to the inconsistency of the findings between the MR analysis and observational study. First, the change in sleep-traits, in most cases, did not suffice to entail an arthroplasty revision in within a short period. Second, these studies had short review or follow-up periods, small sample sizes, and selection bias, resulting in missing of patient data. Moreover, arthroplasty cases were not included in the outcome study. Third, some patients experienced transient sleep disturbances related to surgery, which could improve or worsen after the procedure [ 23 ]. Fourth, previous studies often attributed all sleep problems to overall sleep quality, overlooking the specific impacts of various sleep traits on post-TKA outcomes. Thus, there is a crucial need to break down and separately examine the specific effects of sleep characteristics on post-TKA patients to gain a comprehensive understanding of these effects in prospective clinical studies [ 64 ]. Finally, observational studies rely on self-reported symptoms in surgical patients, which can be influenced by social, psychological, and surgical factors, leading to confounding variables. This Mendelian study attempted to minimize these confounding factors. In summary, identifying risk factors for revision surgery after knee replacement surgery is challenging due to the high survival rate of patients and the various confounding factors identified in previous studies.

Clinical significance and possible mechanisms

Clinical significance.

The identification of significant differences between sleep duration and the need for revision surgery, as elucidated through Mendelian randomization, highlights a potentially causal relationship warranting meticulous analysis. These results implied that variations in sleep duration might have a consequential effect on the probability of requiring revision knee arthroplasty, underscoring the critical role of sleep patterns in both postoperative recovery and longevity of the implant. However, further exploration is needed to ascertain the extent of this effect and to decipher the underlying biological mechanisms. However, preliminary evidence suggested that enhancing sleep quality could be an innovative preventive measure, possibly reducing the incidence of revision surgeries. This proposition not only heralds new opportunities for improving patient outcomes via lifestyle modifications but also necessitates further empirical studies to corroborate these initial findings and to incorporate sleep management strategies into the clinical guidelines for knee arthroplasty care.

Moreover, the application of genetic data in Mendelian randomization provides a solid foundation for confirming causality, facilitating the development of interventions with the potential to markedly influence the clinical management and prognosis of individuals undergoing knee replacement procedures. This approach emphasizes the importance of a comprehensive understanding of the interplay between genetic factors and lifestyle behaviors, such as sleep, from the perspective of surgical outcomes and patient health.

Possible genetic pathways

In this MR analysis, we used specific SNPs as instrumental variables to delineate the causal relationship between sleep duration and the risk for knee arthroplasty revision. Our study suggested that the SNPs might constitute a locus of genetic variation with the potential to modulate biological processes. In the ensuing discourse, we discuss the contributory roles of selected SNPs, such as rs12661667 and rs11763750, to demonstrate how the manner in which genetic predispositions associated with sleep traits may influence postoperative prognoses.

The SNP rs12661667 is located within the USP49 gene and is implicated in the suppression of the PIK3R2/AKT signaling pathway [ 65 ]. The attenuation of the PIK3R2/AKT pathway has been shown to induce oxidative stress subsequent to chronic sleep deprivation [ 66 ]. Furthermore, USP49 has been linked to the modulation of circadian rhythms, potentially impacting both sleep duration and quality [ 35 ]. Perturbations in circadian regulation are known to be associated with inflammatory processes that may retard recovery and amplify the risk of postoperative complications in knee arthroplasty patients. Additionally, the variant rs11763750, residing within the MAD1L1 gene, is correlated with systemic inflammation [ 67 ]—a pivotal determinant of both the recuperation and enduring success of joint replacement interventions.

On the basis of the biological functionality of the SNPs, we proposed a tentative genetic schema that illuminates the molecular interactions possibly underpinning the correlation between sleep duration and the incidence of revision after knee arthroplasty. It must be acknowledged, however, that this exposition is conjectural and contingent upon the extant corpus of knowledge that is subject to evolution with the advent of new empirical evidence. Consequently, prospective functional investigations are needed to substantiate these postulated associations and explicate the mechanisms whereby genetic variations related to sleep may change surgical outcomes.

Possible mechanisms

A short sleep duration has been linked to various physiological and psychological factors that may impact TKA outcomes and increase the risk for revision. One potential mechanism is the effect of insufficient sleep on pain perception. Inadequate sleep has been associated with reduced pain tolerance, hyperalgesia, and central sensitization [ 68 ]. In the context of TKA, individuals with shorter sleep durations may have increased pain levels [ 16 ], leading to dissatisfaction and a greater likelihood of revision surgery.

Furthermore, inadequate sleep can disrupt the body’s natural healing processes. Sleep plays a crucial role in tissue repair and metabolic rate [ 69 ]. Insufficient sleep may hinder these processes and aggravate systemic inflammation [ 70 ], resulting in delayed wound healing, impaired tissue regeneration, and compromised overall recovery from TKA. As a result, individuals with short sleep durations may experience prolonged recovery periods and an increased risk for complications, potentially necessitating revision surgery.

Moreover, sleep deprivation can impact cognitive function and decision-making abilities. A lack of sleep has been found to be associated with cognitive impairments, including reduced attention, impaired memory and fatigue [ 71 ]. TKA patients with impaired cognitive function may struggle to adhere to postoperative care instructions, such as medication schedules, physical therapy exercises, and weight-bearing restrictions [ 72 ]. Non-compliance with these essential aspects of recovery can lead to suboptimal outcomes and a greater risk for revision.

In addition, short sleep durations are associated with fewer naive T cells [ 73 ] and a weakened immune system, which could increase susceptibility to infections [ 74 ]. Moreover, infections are among the most common causes of knee arthroplasty revision.

Additionally, sleep disturbances, including anxiety and depressive disorders, have been identified as contributing factors to the development of psychological distress [ 75 ]. Prior research has consistently shown that depression and anxiety have deleterious effects on well-being across a wide range of demographic groups, irrespective of race, geographical location, or sex [ 76 , 77 , 78 , 79 , 80 ]. Post-TKA patients with short sleep durations may be more prone to these psychological conditions, which can negatively impact their overall well-being and recovery. Furthermore, psychological distress was found to be linked to inferior surgical outcomes and an increased utilization of healthcare resources [ 81 ], potentially elevating the risk of requiring revision surgery. This body of evidence underscores the importance of addressing sleep disturbances and psychological well-being as an integral component of postoperative care to improve patient outcomes and reduce the burden on healthcare systems.

In summary, several mechanisms can explain how short sleep duration may contribute to knee arthroplasty revision. These include heightened pain perception, impaired healing and recovery, a weakened immune response, inflammation, cognitive impairments affecting adherence to postoperative care, and the development of psychological distress. However, further research is necessary to better comprehend the complex mechanism linking sleep traits and TKA outcomes. This understanding will help improve patient care and potentially reduce the rate of revision surgery. The potential underlying mechanisms are shown in Fig.  6 .

Graphic abstract. Bidirectional causal relationships between sleep traits and knee arthroplasty revision and potential underlying mechanisms. This MR study provided limited evidence supporting the use of sleep-improving strategies for preventing knee arthroplasty revision. Moreover, the findings added to the evidence that knee arthroplasty revision does not exert effect on sleep traits. MR: Mendelian randomization

Strengths and limitations

Mendelian randomization (MR) is an analytical method utilized in epidemiology that employs genetic variants as instrumental variables to estimate the causal effect of an exposure on an outcome. This approach offers a powerful tool for causal inference and has several distinct advantages and limitations. One of the key strengths of MR is its ability to reduce the impact of confounding factors compared to traditional observational studies. This is achieved through the random assignment of genetic variants at conception. Consequently, the allocation of these variants mimics the conditions of a randomized controlled trial, ensuring that the genetic variants associated with the exposure of interest are not influenced by confounding variables [ 82 ]. Another crucial aspect of MR is its capacity to establish a clear temporal relationship between the exposure and the outcome. MR addresses concerns regarding reverse causation, a phenomenon that can complicate the interpretation of observational studies. By employing genetic variants as proxies for exposures, MR helps mitigate the uncertainty surrounding whether the exposure affects the outcome or vice versa. This approach provides a more robust framework for drawing causal inferences [ 30 ]. Furthermore, MR is extensively applicable because it can be effectively employed to investigate the causal effects of exposures that are challenging to manipulate in randomized trials due to ethical, practical, or financial constraints. This approach has widened the scope of related research and has facilitated the examination of various exposures and outcomes [ 83 ].

With regard to Mendelian randomization studies, the Instrumental Variable method, notably the IVW approach, is a primary methodology for estimating the causal effect of an exposure on an outcome. Despite the fact that it effectively facilitates causal inference, the IVW method is susceptible to several biases that can compromise the validity of its estimates. One such bias is pleiotropy, which arises when the genetic variants serving as instruments influence the outcome through pathways unrelated to the exposure of interest. Horizontal pleiotropy can distort IVW estimates if genetic variants impact the outcome via alternative risk factors. To detect and correct for pleiotropic effects, sensitivity analyses, including MR‒Egger regression, were employed [ 84 ]. Another challenge is weak instrument bias, which occurs when the genetic variants employed as instruments account for a minimal proportion of the variance in the exposure. Such scenarios can lead to estimates that bias toward observational associations, which are potentially confounding. This issue is commonly evaluated using the F-statistic, with an F-statistic less than 10 indicating a possibly weak instrument [ 85 ]. Selection bias represents another potential concern, as it emerges when the study population does not reflect the general population due to the selection criteria applied. For instance, a propensity to include individuals possessing both the exposure and outcome may skew the IVW estimates [ 86 ]. Additionally, overlapping samples can introduce bias. Specifically, utilizing the same individuals to estimate genetic associations with both the exposure and the outcome can result in overfitting and, consequently, biased estimates—a phenomenon referred to as “sample overlap bias”. Employing non-overlapping samples for the analyses of exposure and outcome associations can mitigate this issue [ 87 ]. Last, the dynamic consequences of the exposure on the outcome, such as effects that vary over time or are contingent upon the exposure level, pose a challenge. Linear models such as IVW may not adequately capture these complex relationships, potentially leading to biased estimates [ 88 ].

In addition, this study is also subject to other potential limitations. First, the validity and sensitivity of the results were not ascertained in some GWASs since they used categorical variables and self-reported sleep phenotypes rather than objective sleep assessments. Second, estimates from previous studies might not be similar to those from observational studies since genetic variants used as a proxy for exposure reflect exposure across a lifetime rather than the exposure at a single measuring occasion. Third, as the summary statistics we used were based primarily on one cohort, the results for knee arthroplasty revision and short sleep duration may be skewed by the winner’s curse. Future studies should include robust replication of these findings. Fourth, the synergistic effects of chronotype, snoring, sleep duration, sleep apnea, and insomnia on knee arthroplasty need further investigation. Additional studies are warranted to determine the roles of sleep phenotypes. Besides, the database utilized for our Mendelian Randomization (MR) study lacks specific classifications for the reasons behind TKA revisions and sleep traits, which restricted our analysis to broader categorizations of sleep phenotypes and joint revision without delving into the specific etiological factors in question. The dataset we utilized did not include detailed patient characteristics such as gender, age, BMI, or the timing of prosthesis revision. Future studies need to incorporate more detailed classification and longitudinal data to provide deeper insights into these relationships as the GWAS database develops. Finally, accessing data bias may occur if the study population is not representative of the general population. Population stratification can skew results in MR studies [ 89 , 90 ]. Using within-family MR [ 91 ], which analyzes data from family members like sibling pairs or parent-child trios, helps control these biases by accounting for genetic variations within families [ 90 ]. However, this approach is limited by the scarcity of family-based GWAS data [ 30 ]. Expanding family-based GWAS could improve MR’s accuracy by integrating family structures, thus enhancing causal inferences [ 92 ]. To minimize this, we used GWAS summary statistics from large, well-characterized cohorts of European ancestry, which reduces the potential for population stratification. Meanwhile, we have taken some steps to ensure data integrity, such as sensitivity analyses, including MR-Egger regression, to detect and correct for pleiotropic effects and calculated F-statistics to evaluate and mitigate weak instrument bias. We also used non-overlapping samples to minimize sample overlap bias. However, we acknowledge that future studies should aim to include diverse populations, and collecting more family-level data is vital to develop this method further and address stratification issues in conventional MR.

Despite these limitations, Mendelian randomization continues to be a valuable tool in the arsenal of epidemiological methods for causal inference. It serves as a complementary approach to traditional observational studies and randomized controlled trials, offering unique insights that may be difficult to obtain through alternative means. First, the large sample sizes achieved through our two-sample MR approach, combined with the use of genetic variants as instrumental variables, reduced susceptibility to reverse causation and residual confounding relative to conventional observational studies. Second, examining only exposures genetically predicted from European populations avoided potential bias from confounding by demographic factors such as ancestry. This strengthens the internal validity of the findings. Finally, the analyses were controlled for horizontal pleiotropy and heterogeneity across genetic variants through multiple sensitivity analyses, adding to the robustness of the conclusions regarding causal effects. In summary, the key strengths included the MR study design, large homogeneous sample, and sensitivity analyses controlled for potential validity threats. Moreover, the current analysis utilized extensive publicly available GWAS data, which provided a substantial sample size. This large sample size enhanced the power of our study, allowing for reliable estimation of lifelong causality.

In general, our study involved two-sample MR analyses, which yielded comprehensive screening data on the causal associations between sleep traits and revision after knee arthroplasty. However, reverse MR analysis did not establish a causal link between knee arthroplasty and sleep traits. These findings highlighted the crucial role of sleep duration in the knee arthroplasty revision. Understanding this correlation has significant clinical implications, since short sleep duration (average sleep duration of 24 h is 6 h or less) has been identified as a potential therapeutic target for preventing knee arthroplasty revision.

The data used in the MR analyses were publicly available and did not require specific ethical approval. The summary GWAS data of the sleep-related traits are available at: https://www.ebi.ac.uk/gwas/home and http://sleepdisordergenetics.org/ . The summary statistics concerning the knee arthroplasty revision GWAS are available at: https://gwas.mrcieu.ac.uk/ .

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Acknowledgements

We are indebted to the participants and investigators of the FinnGen, IEU OpenGWAS project, UK Biobank study, and the NHGRI-EBI Catalog of Human Genome-Wide Association Studies for their support. We also sincerely thank Yan Xu, Wei Qiang, Rui Li, Chong Li, Yu Liu, Zhijun, Bi and Hao Yang for their suggestions for the article.

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Department of Orthopaedic Surgery, Orthopaedic Center, The First Hospital of Jilin University, Changchun, Jilin, 130021, China

Zhiguo Bi, Jintian Chen, Xiaotong Shi, Shiyu Liao, Long Jin & Jianguo Liu

College of Basic Medical Sciences, Jilin University, Changchun, Jilin, 130021, China

Guangdong Provincial Hospital of Chinese Medicine-Zhuhai Hospital, Zhuhai, China

Lithotriptic Center, The First Hospital of Jilin University, Changchun, China

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Z.B.: Conceived and presented the idea of the study; Z.B. & Y.C.: processed the data and wrote the manuscript; Y.C.: participated in the acquisition and interpretation of the data. All the listed authors have made significant intellectual contributions to the research, approved its claims and agreed to be listed as authors. All authors have read and approved the final manuscript.

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Correspondence to Jintian Chen , Long Jin or Jianguo Liu .

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The current MR investigation did not require specific ethical approval because it used publicly accessible summary data. The studies were conducted with the approval of the respective institutional ethical review boards and in accordance with the Declaration of Helsinki.

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Bi, Z., Cai, Y., Chen, J. et al. Genetically predicted effects of 10 sleep phenotypes on revision of knee arthroplasty: a mendelian randomization study. J Orthop Surg Res 19 , 563 (2024). https://doi.org/10.1186/s13018-024-05031-0

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Received : 25 June 2024

Accepted : 25 August 2024

Published : 12 September 2024

DOI : https://doi.org/10.1186/s13018-024-05031-0

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