research method that cause and effect

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.

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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.

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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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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.

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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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Market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, qualitative vs quantitative research 13 min read, qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, request demo.

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Establishing Cause and Effect

A central goal of most research is the identification of causal relationships, or demonstrating that a particular independent variable (the cause) has an effect on the dependent variable of interest (the effect).  The three criteria for establishing cause and effect – association, time ordering (or temporal precedence), and non-spuriousness – are familiar to most researchers from courses in research methods or statistics.  While the classic examples used to illustrate these criteria may imply that establishing cause and effect is straightforward, it is often one of the most challenging aspects of designing research studies for implementation in real world conditions.

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The first step in establishing causality is demonstrating association; simply put, is there a relationship between the independent variable and the dependent variable?  If both variables are numeric, this can be established by looking at the correlation between the two to determine if they appear to convey.  A common example is the relationship between education and income: in general, individuals with more years of education are also likely to earn higher incomes.  Cross tabulation, which cross-classifies the distributions of two categorical variables, can also be used to examination association.  For example, we may observe that 60% of Protestants support the death penalty while only 35% of Catholics do so, establishing an association between denomination and attitudes toward capital punishment.  There is ongoing debate regarding just how closely associated variables must be to make a causal claim, but in general researchers are more concerned with the statistical significance of an association (whether it is likely to exist in the population) than with the actual strength of the association.

Once an association has been established, our attention turns to determining the time order of the variables of interest.  In order for the independent variable to cause the dependent variable, logic dictates that the independent variable must occur first in time; in short, the cause must come before the effect.  This time ordering is easy to ensure in an experimental design where the researcher carefully controls exposure to the treatment (which would be the independent variable) and then measures the outcome of interest (the dependent variable).  In cross-sectional designs the time ordering can be much more difficult to determine, especially when the relationship between variables could reasonably go in the opposite direction.  For example, although education usually precedes income, it is possible that individuals who are making a good living may finally have the money necessary to return to school.  Determining time ordering thus may involve using logic, existing research, and common sense when a controlled experimental design is not possible.  In any case, researchers must be very careful about specifying the hypothesized direction of the relationship between the variables and provide evidence (either theoretical or empirical) to support their claim.

The third criterion for causality is also the most troublesome, as it requires that alternative explanations for the observed relationship between two variables be ruled out.  This is termed non-spuriousness, which simply means “not false.”  A spurious or false relationship exists when what appears to be an association between the two variables is actually caused by a third extraneous variable.  Classic examples of spuriousness include the relationship between children’s shoe sizes and their academic knowledge: as shoe size increases so does knowledge, but of course both are also strongly related to age.  Another well-known example is the relationship between the number of fire fighters that respond to a fire and the amount of damage that results – clearly, the size of the fire determines both, so it is inaccurate to say that more fire fighters cause greater damage.  Though these examples seem straightforward, researchers in the fields of psychology, education, and the social sciences often face much greater challenges in ruling out spurious relationships simply because there are so many other factors that might influence the relationship between two variables.  Appropriate study design (using experimental procedures whenever possible), careful data collection and use of statistical controls, and triangulation of many data sources are all essential when seeking to establish non-spurious relationships between variables.

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• Correlation vs. Causation | Difference, Designs & Examples

Correlation vs. Causation | Difference, Designs & Examples

Published on July 12, 2021 by Pritha Bhandari . Revised on June 22, 2023.

Correlation means there is a statistical association between variables. Causation means that a change in one variable causes a change in another variable.

In research, you might have come across the phrase “correlation doesn’t imply causation.” Correlation and causation are two related ideas, but understanding their differences will help you critically evaluate sources and interpret scientific research.

Table of contents

What’s the difference, why doesn’t correlation mean causation, correlational research, third variable problem, regression to the mean, spurious correlations, directionality problem, causal research, other interesting articles, frequently asked questions about correlation and causation.

Correlation describes an association between types of variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables. These variables change together: they covary. But this covariation isn’t necessarily due to a direct or indirect causal link.

Causation means that changes in one variable brings about changes in the other; there is a cause-and-effect relationship between variables. The two variables are correlated with each other and there is also a causal link between them.

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There are two main reasons why correlation isn’t causation. These problems are important to identify for drawing sound scientific conclusions from research.

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not. For example, ice cream sales and violent crime rates are closely correlated, but they are not causally linked with each other. Instead, hot temperatures, a third variable, affects both variables separately. Failing to account for third variables can lead research biases to creep into your work.

The directionality problem occurs when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other. For example, vitamin D levels are correlated with depression, but it’s not clear whether low vitamin D causes depression, or whether depression causes reduced vitamin D intake.

You’ll need to use an appropriate research design to distinguish between correlational and causal relationships:

• Correlational research designs can only demonstrate correlational links between variables.
• Experimental designs can test causation.

In a correlational research design, you collect data on your variables without manipulating them.

Correlational research is usually high in external validity , so you can generalize your findings to real life settings. But these studies are low in internal validity , which makes it difficult to causally connect changes in one variable to changes in the other.

These research designs are commonly used when it’s unethical, too costly, or too difficult to perform controlled experiments. They are also used to study relationships that aren’t expected to be causal.

Without controlled experiments, it’s hard to say whether it was the variable you’re interested in that caused changes in another variable. Extraneous variables are any third variable or omitted variable other than your variables of interest that could affect your results.

Limited control in correlational research means that extraneous or confounding variables serve as alternative explanations for the results. Confounding variables can make it seem as though a correlational relationship is causal when it isn’t.

When two variables are correlated, all you can say is that changes in one variable occur alongside changes in the other.

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Regression to the mean is observed when variables that are extremely higher or extremely lower than average on the first measurement move closer to the average on the second measurement. Particularly in research that intentionally focuses on the most extreme cases or events, RTM should always be considered as a possible cause of an observed change.

Players or teams featured on the cover of SI have earned their place by performing exceptionally well. But athletic success is a mix of skill and luck, and even the best players don’t always win.

Chances are that good luck will not continue indefinitely, and neither can exceptional success.

A spurious correlation is when two variables appear to be related through hidden third variables or simply by coincidence.

The Theory of the Stork draws a simple causal link between the variables to argue that storks physically deliver babies. This satirical study shows why you can’t conclude causation from correlational research alone.

When you analyze correlations in a large dataset with many variables, the chances of finding at least one statistically significant result are high. In this case, you’re more likely to make a type I error . This means erroneously concluding there is a true correlation between variables in the population based on skewed sample data.

To demonstrate causation, you need to show a directional relationship with no alternative explanations. This relationship can be unidirectional, with one variable impacting the other, or bidirectional, where both variables impact each other.

A correlational design won’t be able to distinguish between any of these possibilities, but an experimental design can test each possible direction, one at a time.

• Physical activity may affect self esteem
• Self esteem may affect physical activity
• Physical activity and self esteem may both affect each other

In correlational research, the directionality of a relationship is unclear because there is limited researcher control. You might risk concluding reverse causality, the wrong direction of the relationship.

Causal links between variables can only be truly demonstrated with controlled experiments . Experiments test formal predictions, called hypotheses , to establish causality in one direction at a time.

Experiments are high in internal validity , so cause-and-effect relationships can be demonstrated with reasonable confidence.

You can establish directionality in one direction because you manipulate an independent variable before measuring the change in a dependent variable.

In a controlled experiment, you can also eliminate the influence of third variables by using random assignment and control groups.

Random assignment helps distribute participant characteristics evenly between groups so that they’re similar and comparable. A control group lets you compare the experimental manipulation to a similar treatment or no treatment (or a placebo, to control for the placebo effect ).

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.

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.

Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them.

While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy .

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

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What is causal research design?

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Examining these relationships gives researchers valuable insights into the mechanisms that drive the phenomena they are investigating.

Organizations primarily use causal research design to identify, determine, and explore the impact of changes within an organization and the market. You can use a causal research design to evaluate the effects of certain changes on existing procedures, norms, and more.

This article explores causal research design, including its elements, advantages, and disadvantages.

Analyze your causal research

Dovetail streamlines causal research analysis to help you uncover and share actionable insights

• Components of causal research

You can demonstrate the existence of cause-and-effect relationships between two factors or variables using specific causal information, allowing you to produce more meaningful results and research implications.

These are the key inputs for causal research:

The timeline of events

Ideally, the cause must occur before the effect. You should review the timeline of two or more separate events to determine the independent variables (cause) from the dependent variables (effect) before developing a hypothesis. 

If the cause occurs before the effect, you can link cause and effect and develop a hypothesis .

For instance, an organization may notice a sales increase. Determining the cause would help them reproduce these results. 

Upon review, the business realizes that the sales boost occurred right after an advertising campaign. The business can leverage this time-based data to determine whether the advertising campaign is the independent variable that caused a change in sales. 

Evaluation of confounding variables

In most cases, you need to pinpoint the variables that comprise a cause-and-effect relationship when using a causal research design. This uncovers a more accurate conclusion. 

Co-variations between a cause and effect must be accurate, and a third factor shouldn’t relate to cause and effect. 

Observing changes

Variation links between two variables must be clear. A quantitative change in effect must happen solely due to a quantitative change in the cause. 

You can test whether the independent variable changes the dependent variable to evaluate the validity of a cause-and-effect relationship. A steady change between the two variables must occur to back up your hypothesis of a genuine causal effect. 

• Why is causal research useful?

Causal research allows market researchers to predict hypothetical occurrences and outcomes while enhancing existing strategies. Organizations can use this concept to develop beneficial plans. 

Causal research is also useful as market researchers can immediately deduce the effect of the variables on each other under real-world conditions. 

Once researchers complete their first experiment, they can use their findings. Applying them to alternative scenarios or repeating the experiment to confirm its validity can produce further insights. 

Businesses widely use causal research to identify and comprehend the effect of strategic changes on their profits. 

• How does causal research compare and differ from other research types?

Other research types that identify relationships between variables include exploratory and descriptive research . 

Here’s how they compare and differ from causal research designs:

Exploratory research

An exploratory research design evaluates situations where a problem or opportunity's boundaries are unclear. You can use this research type to test various hypotheses and assumptions to establish facts and understand a situation more clearly.

You can also use exploratory research design to navigate a topic and discover the relevant variables. This research type allows flexibility and adaptability as the experiment progresses, particularly since no area is off-limits.

It’s worth noting that exploratory research is unstructured and typically involves collecting qualitative data . This provides the freedom to tweak and amend the research approach according to your ongoing thoughts and assessments. 

Unfortunately, this exposes the findings to the risk of bias and may limit the extent to which a researcher can explore a topic. 

This table compares the key characteristics of causal and exploratory research:

Descriptive research

This research design involves capturing and describing the traits of a population, situation, or phenomenon. Descriptive research focuses more on the " what " of the research subject and less on the " why ."

Since descriptive research typically happens in a real-world setting, variables can cross-contaminate others. This increases the challenge of isolating cause-and-effect relationships. 

You may require further research if you need more causal links. 

This table compares the key characteristics of causal and descriptive research.  

Causal research examines a research question’s variables and how they interact. It’s easier to pinpoint cause and effect since the experiment often happens in a controlled setting. 

Researchers can conduct causal research at any stage, but they typically use it once they know more about the topic.

In contrast, causal research tends to be more structured and can be combined with exploratory and descriptive research to help you attain your research goals. 

• How can you use causal research effectively?

Here are common ways that market researchers leverage causal research effectively:

Market and advertising research

Do you want to know if your new marketing campaign is affecting your organization positively? You can use causal research to determine the variables causing negative or positive impacts on your campaign. 

Improving customer experiences and loyalty levels

Consumers generally enjoy purchasing from brands aligned with their values. They’re more likely to purchase from such brands and positively represent them to others. 

You can use causal research to identify the variables contributing to increased or reduced customer acquisition and retention rates. 

Could the cause of increased customer retention rates be streamlined checkout? 

Perhaps you introduced a new solution geared towards directly solving their immediate problem. 

Whatever the reason, causal research can help you identify the cause-and-effect relationship. You can use this to enhance your customer experiences and loyalty levels.

Improving problematic employee turnover rates

Is your organization experiencing skyrocketing attrition rates? 

You can leverage the features and benefits of causal research to narrow down the possible explanations or variables with significant effects on employees quitting. 

This way, you can prioritize interventions, focusing on the highest priority causal influences, and begin to tackle high employee turnover rates. 

• Advantages of causal research

The main benefits of causal research include the following:

Effectively test new ideas

If causal research can pinpoint the precise outcome through combinations of different variables, researchers can test ideas in the same manner to form viable proof of concepts.

Achieve more objective results

Market researchers typically use random sampling techniques to choose experiment participants or subjects in causal research. This reduces the possibility of exterior, sample, or demography-based influences, generating more objective results. 

Improved business processes

Causal research helps businesses understand which variables positively impact target variables, such as customer loyalty or sales revenues. This helps them improve their processes, ROI, and customer and employee experiences.

Guarantee reliable and accurate results

Upon identifying the correct variables, researchers can replicate cause and effect effortlessly. This creates reliable data and results to draw insights from. 

Internal organization improvements

Businesses that conduct causal research can make informed decisions about improving their internal operations and enhancing employee experiences. 

• Disadvantages of causal research

Like any other research method, casual research has its set of drawbacks that include:

Extra research to ensure validity

Researchers can't simply rely on the outcomes of causal research since it isn't always accurate. There may be a need to conduct other research types alongside it to ensure accurate output.

Coincidence

Coincidence tends to be the most significant error in causal research. Researchers often misinterpret a coincidental link between a cause and effect as a direct causal link. 

Administration challenges

Causal research can be challenging to administer since it's impossible to control the impact of extraneous variables . 

Giving away your competitive advantage

If you intend to publish your research, it exposes your information to the competition. 

Competitors may use your research outcomes to identify your plans and strategies to enter the market before you. 

• Causal research examples

Multiple fields can use causal research, so it serves different purposes, such as. 

Customer loyalty research

Organizations and employees can use causal research to determine the best customer attraction and retention approaches. 

They monitor interactions between customers and employees to identify cause-and-effect patterns. That could be a product demonstration technique resulting in higher or lower sales from the same customers. 

Example: Business X introduces a new individual marketing strategy for a small customer group and notices a measurable increase in monthly subscriptions. 

Upon getting identical results from different groups, the business concludes that the individual marketing strategy resulted in the intended causal relationship.

Advertising research

Businesses can also use causal research to implement and assess advertising campaigns. 

Example: Business X notices a 7% increase in sales revenue a few months after a business introduces a new advertisement in a certain region. The business can run the same ad in random regions to compare sales data over the same period. 

This will help the company determine whether the ad caused the sales increase. If sales increase in these randomly selected regions, the business could conclude that advertising campaigns and sales share a cause-and-effect relationship. 

Educational research

Academics, teachers, and learners can use causal research to explore the impact of politics on learners and pinpoint learner behavior trends. 

Example: College X notices that more IT students drop out of their program in their second year, which is 8% higher than any other year. 

The college administration can interview a random group of IT students to identify factors leading to this situation, including personal factors and influences. 

With the help of in-depth statistical analysis, the institution's researchers can uncover the main factors causing dropout. They can create immediate solutions to address the problem.

Is a causal variable dependent or independent?

When two variables have a cause-and-effect relationship, the cause is often called the independent variable. As such, the effect variable is dependent, i.e., it depends on the independent causal variable. An independent variable is only causal under experimental conditions. 

What are the three criteria for causality?

The three conditions for causality are:

Temporality/temporal precedence: The cause must precede the effect.

Rationality: One event predicts the other with an explanation, and the effect must vary in proportion to changes in the cause.

Control for extraneous variables: The covariables must not result from other variables.  

Is causal research experimental?

Causal research is mostly explanatory. Causal studies focus on analyzing a situation to explore and explain the patterns of relationships between variables. 

Further, experiments are the primary data collection methods in studies with causal research design. However, as a research design, causal research isn't entirely experimental.

What is the difference between experimental and causal research design?

One of the main differences between causal and experimental research is that in causal research, the research subjects are already in groups since the event has already happened. 

On the other hand, researchers randomly choose subjects in experimental research before manipulating the variables.

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Causal Research (Explanatory research)

Causal research, also known as explanatory research is conducted in order to identify the extent and nature of cause-and-effect relationships. Causal research can be conducted in order to assess impacts of specific changes on existing norms, various processes etc.

Causal studies focus on an analysis of a situation or a specific problem to explain the patterns of relationships between variables. Experiments  are the most popular primary data collection methods in studies with causal research design.

The presence of cause cause-and-effect relationships can be confirmed only if specific causal evidence exists. Causal evidence has three important components:

1. Temporal sequence . The cause must occur before the effect. For example, it would not be appropriate to credit the increase in sales to rebranding efforts if the increase had started before the rebranding.

2. Concomitant variation . The variation must be systematic between the two variables. For example, if a company doesn’t change its employee training and development practices, then changes in customer satisfaction cannot be caused by employee training and development.

3. Nonspurious association . Any covarioaton between a cause and an effect must be true and not simply due to other variable. In other words, there should be no a ‘third’ factor that relates to both, cause, as well as, effect.

The table below compares the main characteristics of causal research to exploratory and descriptive research designs: [1]

Main characteristics of research designs

 Examples of Causal Research (Explanatory Research)

The following are examples of research objectives for causal research design:

• To assess the impacts of foreign direct investment on the levels of economic growth in Taiwan
• To analyse the effects of re-branding initiatives on the levels of customer loyalty
• To identify the nature of impact of work process re-engineering on the levels of employee motivation

Advantages of Causal Research (Explanatory Research)

• Causal studies may play an instrumental role in terms of identifying reasons behind a wide range of processes, as well as, assessing the impacts of changes on existing norms, processes etc.
• Causal studies usually offer the advantages of replication if necessity arises
• This type of studies are associated with greater levels of internal validity due to systematic selection of subjects

Disadvantages of Causal Research (Explanatory Research)

• Coincidences in events may be perceived as cause-and-effect relationships. For example, Punxatawney Phil was able to forecast the duration of winter for five consecutive years, nevertheless, it is just a rodent without intellect and forecasting powers, i.e. it was a coincidence.
• It can be difficult to reach appropriate conclusions on the basis of causal research findings. This is due to the impact of a wide range of factors and variables in social environment. In other words, while casualty can be inferred, it cannot be proved with a high level of certainty.
• It certain cases, while correlation between two variables can be effectively established; identifying which variable is a cause and which one is the impact can be a difficult task to accomplish.

My e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance  contains discussions of theory and application of research designs. The e-book also explains all stages of the  research process  starting from the  selection of the research area  to writing personal reflection. Important elements of dissertations such as  research philosophy ,  research approach ,  methods of data collection ,  data analysis  and  sampling  are explained in this e-book in simple words.

John Dudovskiy

[1] Source: Zikmund, W.G., Babin, J., Carr, J. & Griffin, M. (2012) “Business Research Methods: with Qualtrics Printed Access Card” Cengage Learning

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Methods for Evaluating Causality in Observational Studies

Emilio a.l.gianicolo.

1 Institute for Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center of the Johannes Gutenberg University of Mainz

2 Institute of Clinical Physiology of the Italian National Research Council, Lecce, Italy

Martin Eichler

3 Technical University Dresden, University Hospital Carl Gustav Carus, Medical Clinic 1, Dresden

Oliver Muensterer

4 Department of Pediatric Surgery, Faculty of Medicine, Johannes Gutenberg University of Mainz

Konstantin Strauch

5 Institute of Genetic Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg; Chair of Genetic Epidemiology, Institute for Medical Information Processing, Biometry, and Epidemiology, Ludwig-Maximilians-Universität, München

Maria Blettner

In clinical medical research, causality is demonstrated by randomized controlled trials (RCTs). Often, however, an RCT cannot be conducted for ethical reasons, and sometimes for practical reasons as well. In such cases, knowledge can be derived from an observational study instead. In this article, we present two methods that have not been widely used in medical research to date.

The methods of assessing causal inferences in observational studies are described on the basis of publications retrieved by a selective literature search.

Two relatively new approaches—regression-discontinuity methods and interrupted time series—can be used to demonstrate a causal relationship under certain circumstances. The regression-discontinuity design is a quasi-experimental approach that can be applied if a continuous assignment variable is used with a threshold value. Patients are assigned to different treatment schemes on the basis of the threshold value. For assignment variables that are subject to random measurement error, it is assumed that, in a small interval around a threshold value, e.g., cholesterol values of 160 mg/dL, subjects are assigned essentially at random to one of two treatment groups. If patients with a value above the threshold are given a certain treatment, those with values below the threshold can serve as control group. Interrupted time series are a special type of regression-discontinuity design in which time is the assignment variable, and the threshold is a cutoff point. This is often an external event, such as the imposition of a smoking ban. A before-and-after comparison can be used to determine the effect of the intervention (e.g., the smoking ban) on health parameters such as the frequency of cardiovascular disease.

The approaches described here can be used to derive causal inferences from observational studies. They should only be applied after the prerequisites for their use have been carefully checked.

The fact that correlation does not imply causality was frequently mentioned in 2019 in the public debate on the effects of diesel emission exposure ( 1 , 2 ). This truism is well known and generally acknowledged. A more difficult question is how causality can be unambiguously defined and demonstrated ( box 1 ) . According to the eighteenth-century philosopher David Hume, causality is present when two conditions are satisfied: 1) B always follows A—in which case, A is called a “sufficient cause” of B; 2) if A does not occur, then B does not occur—in which case, A is called a “necessary cause” of B ( 3 ). These strict logical criteria are only rarely met in the medical field. In the context of exposure to diesel emissions, they would be met only if fine-particle exposure always led to lung cancer, and lung cancer never occurred without prior fine-particle exposure. Of course, neither of these is true. So what is biological, medical, or epidemiological causality? In medicine, causality is generally expressed in probabilistic terms, i.e. exposure to a risk factor such as cigarette smoking or diesel emissions increases the probability of a disease, e.g., lung cancer. The same understanding of causality applies to the effects of treatment: for instance, a certain type of chemotherapy increases the likelihood of survival in patients with a diagnosis of cancer, but does not guarantee it.

Causality in epidemiological observational studies (modified from Parascondola and Weed [34])

• ausality as production: A produces B. Causality is to be distinguished from mere temporal sequence. It does not suffice to note that A is always followed by B; rather, A must in some way produce, lead to, or create B. However, it remains unclear what ’producing’, ‘leading to’, or ‘creating’ exactly means. On a practical level, the notion of production is what is illustrated in the diagrams of cause-and-effect relationships that are commonly seen in medical publications.
• Sufficient and necessary causes: A is a sufficient cause of B if B always happens when A has happened. A is a necessary cause of B if B only happens when A has happened. Although these relationships are logically clear and seemingly simple, this type of deterministic causality is hardly ever found in real-life scientific research. Thus, smoking is neither a sufficient nor a necessary cause of lung cancer. Smoking is not always followed by lung cancer (not a sufficient cause), and lung cancer can occur in the absence of tobacco exposure (not a necessary cause, either).
• Sufficient component cause: This notion was developed in response to the definitions of sufficient and necessary causes. In this approach, it is assumed that multiple causes act together to produce an effect where no single one of them could do so alone. There can also be different combinations of causes that produce the same effect.
• Probabilistic causality: In this scenario, the cause (A) increases the probability (P) that the effect (B) will occur: in symbols, P (B | A) > (B | not A). Sufficient and necessary causes, as defined above in ( 2 ), are only those extreme cases in which P (B | A) = 1 and P (B | not A) = 0, respectively. When these probabilities take on values that are neither 0 nor 1, causality is no longer deterministic, but rather probabilistic (stochastic). There is no assumption that a cause must be followed by an effect. This viewpoint corresponds to the method of proceeding in statistically oriented scientific disciplines.
• Causal inference: This is the determination that a causal relationship exists between two types of event. Causal inferences are made by analyzing the changes in the effect that arise when there are changes in the cause. Causal inference goes beyond the mere assertion of an association and is connected to a number of specific concepts: some that have been widely discussed recently are counterfactuals, potential outcomes, causal diagrams, and structural equation models ( 36 , 37 ).
• Triangulation: Not all questions can be answered with an experiment or a randomized controlled trial. Alternatively, methodological pluralism is needed, or, as it is now sometimes called, triangulation: confidence in a finding increases when the same finding is arrived at from multiple data sets, multiple scientific disciplines, multiple theories, and/or multiple methods ( 35 ).
• The criterion of consequentiality: The claim that a causal relationship exists has consequences on a societal level (taking action or not taking action). Olsen has called for the formulation of a criterion to determine when action should be taken and when not ( 7 ).

In many scientific disciplines, causality must be demonstrated by an experiment. In clinical medical research, this purpose is achieved with a randomized controlled trial (RCT) ( 4 ). An RCT, however, often cannot be conducted for either ethical or practical reasons. If a risk factor such as exposure to diesel emissions is to be studied, persons cannot be randomly allocated to exposure or non-exposure. Nor is any randomization possible if the research question is whether or not an accident associated with an exposure, such as the Chernobyl nuclear reactor disaster, increased the frequency of illness or death. The same applies when a new law or regulation, e.g., a smoking ban, is introduced.

When no experiment can be conducted, observational studies need to be performed. The object under study—i.e., the possible cause—cannot be varied in a targeted and controlled way; instead, the effect this factor has on a target variable, such as a particular illness, is observed and documented.

Several publications in epidemiology have dealt with the ways in which causality can be inferred in the absence of an experiment, starting with the classic work of Bradford Hill and the nine aspects of causality (viewpoints) that he proposed ( box 2 ) ( 5 ) and continuing up to the present ( 6 , 7 ).

The Bradford Hill criteria for causality (modified from [5])

• Strength: the stronger the observed association between two variables, the less likely it is due to chance.
• Consistency: the association has been observed in multiple studies, populations at risk, places, and times, and by different researchers.
• Specificity: it is a strong argument for causality when a specific population suffers from a specific disease.
• Temporality: the effect must be temporally subsequent to the cause.
• Biological gradient: the association displays a dose–response effect, e.g., the incidence of lung cancer is greater when more cigarettes are smoked per day.
• Plausibility: a plausible mechanism linking the cause to the effect is helpful, but not absolutely required. What is biologically plausible depends upon the state-of-the-art knowledge of the time.
• Coherence: the causal interpretation of the data should not conflict with biological knowledge about the disease.
• Experiment: experimental evidence should be adduced in support, if possible.
• Analogy: an association speaks for causality if similar causes are already known to have similar effects.

Aside from the statistical uncertainty that always arises when only a sample of an affected population is studied, rather than its entirety ( 8 ), the main obstacle to the study of putative causal relationships comes from confounding variables (“confounders”). These are so named because they can, depending on the circumstances, either obscure a true effect or simulate an effect that is, in fact, not present ( 9 ). Age, for example, is a confounder in the study of the association between occupational radiation exposure and cataract ( 10 ), because both cumulative radiation exposure and the risk of cataract rise with increasing age.

The various statistical methods of dealing with known confounders in the analysis of epidemiological data have already been presented in other articles in this series ( 9 , 11 , 12 ). In the current article, we discuss two new approaches that have not been widely applied in medical and epidemiological research to date.

Methods of evaluating causal inferences in observational studies

The main advantage of an RCT is randomization, i.e., the random allocation of the units of observation (patients) to treatment groups. Potential confounders, whether known or unknown, are thereby distributed to the treatment groups at random as well, although differences between groups may arise through sample variance. Whenever randomization is not possible, the effect of confounders must be taken into account in the planning of the study and in data analysis, as well as in the interpretation of study findings.

Classic methods of dealing with confounders in study planning are stratification and matching ( 13 , 14 ), as well as so-called propensity score matching (PSM) ( 11 ).

The best-known and most commonly used method of data analysis is regression analysis, e.g., linear, logistic, or Cox regression ( 15 ). This method is based on a mathematical model created in order to explain the probability that any particular outcome will arise as the combined result of the known confounders and the effect under study.

Regression analyses are used in the analysis of clinical or epidemiological data and are found in all commonly used statistical software packages. However, they are often used inappropriately because the prerequisites for their correct application have not been checked. They should not be used, for example, if the sample is too small, if the number of variables is too large, or if a correlation between the model variables makes the results uninterpretable ( 16 ).

Regression-discontinuity methods

Regression-discontinuity methods have been little used in medical research to date, but they can be helpful in the study of cause-and-effect relationships from observational data ( 17 ). Regression-discontinuity design is a quasi-experimental approach ( box 3 ) that was developed in educational psychology in the 1960s ( 18 ). It can be used when a threshold value of a continuous variable (the “assignment variable”) determines the treatment regimen to which each patient in the study is assigned ( box 4 ) .

Terms used to characterize experiments ( 18 )

• Experiment/trial A study in which an intervention is deliberately introduced in order to observe an effect.
• Randomized experiment/trial An experiment in which persons, patients, or other units of observation are randomly assigned to one of two or more treatment groups (or intervention groups).
• Quasi-experiment An experiment in which the units of observation are not randomly assigned to the treatment/intervention groups.
• Natural experiment A study in which a natural event (e.g., an earthquake) is compared with a comparison scenario.
• Non-experimental observational study A study in which the size and direction of the association between two variables is determined.

In the simplest case, that of a linear regression, the parameters in the following model are to be estimated:

y i = ß 0 + ß 1 z i + ß 2 (x i - x c ) + e i,

i from 1 to N represents the statistical units

y is the outcome

ß 0 is the y-intercept

z is a dichotomous variable (0, ) indicating whether the patient was treated ( 1 ) or not treated (0)

x is the assignment variable

x c is the threshold

ß 1 is the effect of treatment

ß 2 is the regression coefficient of the assignment variable

e is the random error

A possible assignment variable could be, for example, the serum cholesterol level: consider a study in which patients with a cholesterol level of 160 mg/dL or above are assigned to receive a therapy. Since the cholesterol level (the assignment variable) is subject to random measurement error, it can be assumed that patients whose level of cholesterol is close to the threshold (160 mg/dL) are randomly assigned to the different treatment regimens. Thus, in a small interval around the threshold value, the assignment of patients to treatment groups can effectively be considered random ( 18 ). This sample of patients with near-threshold measurements can thus be used for the analysis of treatment efficacy. For this line of argument to be valid, it must truly be the case that the value being measured is subject to measuring error, and that there is practically no difference between persons with measured values slightly below or slightly above threshold. Treatment allocation in this narrow range can be considered quasi-random.

This method can be applied if the following prerequisites are met:

• The assignment variable is a continuous variable that is measured before the treatment is provided. If the assignment variable is totally independent of the outcome and has no biological, medical, or epidemiological significance, the method is theoretically equivalent to an RCT ( 19 ).
• The treatment must not affect the assignment variable ( 18 ).
• The patients in the two treatment groups with near-threshold values of the assignment variable must be shown to be similar in their baseline properties, i.e., covariables, including possible confounders. This can be demonstrated either with statistical techniques or graphically ( 20 ).
• The range of the assignment variable in the vicinity of the threshold must be optimally set: it must be large enough to yield samples of adequate size in the treatment groups, yet small enough that the effect of the assignment variable itself does not alter the outcome being studied. Methods of choosing this range appropriately are available in the literature ( 21 , 22 ).
• The treatment can be decided upon solely on the basis of the assignment variable (deterministic regression-discontinuity methods), or on the basis of other clinical factors (fuzzy regression-discontinuity methods).

Example 1: The one-year mortality of neonates as a function of the intensity of medical and nursing care was to be studied, where the intensity of care was determined by a birth-weight threshold: infants with very low birth weight (<1500 g) (group A) were cared for more intensively than heavier infants (group B) ( 23 ). The question to be answered was whether the greater intensity of care in group A led to a difference in mortality between the two groups. It was assumed that children with birth weight near the threshold are identical in all other respects, and that their assignment to group A or group B is quasi-random, because the measured value (birth weight) is subject to a relatively small error. Thus, for example, one might compare children weighing 1450–1500 g to those weighing 1501–1550 g at birth to study whether, and how, a greater intensity of care affects mortality.

In this example, it is assumed that the variable “birth weight” has a random measuring error, and thus that neonates whose (true) weight is near the threshold will be randomly allocated to one or the other category. But birth weight itself is an important factor affecting infant mortality, with lower birth weight associated with higher mortality ( 23 ); thus, the interval taken around the threshold for the purpose of this study had to be kept narrow. The study, in fact, showed that the children treated more intensively because their birth weight was just below threshold had a lower mortality than those treated less intensively because their birth weight was just above threshold.

Example 2: A regression-discontinuity design was used to evaluate the effect of a measure taken by the Canadian government: the introduction of a minimum age of 19 years for alcohol consumption. The researchers compared the number of alcohol-related disorders and of violent attacks, accidents, and suicides under the influence of alcohol in the months leading up to (group A) and subsequent to (group B) the 19 th birthday of the persons involved. It was found that persons in group B had a greater number of alcohol-related inpatient treatments and emergency hospitalizations than persons in group A. With the aid of this quasi-experimental approach, the researchers were able to demonstrate the success of the measure ( 24 ). It may be assumed that the two groups differed only with respect to age, and not with respect to any other property affecting alcohol consumption.

Interrupted time series

Interrupted time series are a special type of regression-discontinuity design in which time is the assignment variable. The cutoff point is often an external event that is unambiguously identifiable as having occurred at a certain point in time, e.g., an industrial accident or a change in the law. A before-and-after comparison is made in which the analysis must still take adequate account of any relevant secular trends and seasonal fluctuations ( box 5 ) .

In the simplest case of a study involving an interrupted time series, the temporal sequence is analyzed with a piecewise regression. The following model is used to study both a shift in slope and a shift in the level of an outcome before and after an intervention, e.g., the introduction of a law banning smoking ( figure 2 ):

y = ß 0 + ß 1 × time + ß 2 × intervention + ß 3 × time × intervention + e,

y is the outcome, e.g., cardiovascular diseases

intervention is a dummy variable for the time before (0) and after (1) the intervention (e.g., smoking ban)

time is the time since the beginning of the study

ß 0 is the baseline incidence of cardiovascular diseases

ß 1 is the slope in the incidence of cardiovascular diseases over time before the introduction of the smoking ban

ß 2 is the change in the incidence level of cardiovascular diseases after the introduction of the smoking ban (level effect)

ß 3 is the change in the slope over time (cf. ß 1 ) after the introduction of the smoking ban (slope effect)

The prerequisites for the use of this method must be met ( 18 , 25 ):

• Interrupted time series are valid only if a single intervention took place in the period of the study.
• The time before the intervention must be clearly distinguishable from the time after the intervention.
• There is no required minimum number of data points, but studies with only a small number of data points or small effect sizes must be interpreted with caution. The power of a study is greatest when the number of data points before the intervention equals the number after the intervention ( 26 ).
• Although the equation in Box 5 has a linear specification, polynomial and other nonlinear regression models can be used as well. Meticulous study of the temporal sequence is very important when a nonlinear model is used.
• If an observation at time t —e.g., the monthly incidence of cardiovascular diseases—is correlated with previous observations (autoregression), then the appropriate statistical techniques must be used (autoregressive integrated moving average [ARIMA] models).

Example 1: In one study, the rates of acute hospitalization for cardiovascular diseases before and after the temporary closure of Heathrow Airport because of volcanic ash were determined to investigate the putative effect of aircraft noise ( 27 ). The intervention (airport closure) took place from 15 to 20 April 2010. The hospitalization rate was found to have decreased among persons living in the urban area with the most aircraft noise. The number of observation points was too low, however, to show a causal link conclusively.

Example 2: In another study, the rates of hospitalization before and after the implementation of a smoking ban (the intervention) in public areas in Italy were determined ( 28 ). The intervention occurred in January 2004 (the cutoff time). The number of hospitalizations for acute coronary events was measured from January 2002 to November 2006 ( figure 1 ) . The analysis took account of seasonal dependence, and an effect modification for two age groups—persons under age 70 and persons aged 70 and up—was determined as well. The hospitalization rate declined in the former group, but not the latter.

Age-standardized hospitalization rates for acute coronary events (ACE) in persons under age 70 before and after the implementation of a smoking ban in public places in Italy, studied with the corresponding methods ( 30 ). The observed and predicted rates are shown (circles and solid lines, respectively). The dashed lines show the seasonally adjusted trend in ACE before and after the introduction of the nationwide smoking ban.

The necessary distinction between causality and correlation is often emphasized in scientific discussions, yet it is often not applied strictly enough. Furthermore, causality in medicine and epidemiology is mostly probabilistic in nature, i.e., an intervention alters the probability that the event under study will take place. A good illustration of this principle is offered by research on the effects of radiation, in which a strict distinction is maintained between deterministic radiation damage on the one hand, and probabilistic (stochastic) radiation damage on the other ( 29 ). Deterministic radiation damage—radiation-induced burns or death—arises with certainty whenever a subject receives a certain radiation dose (usually a high one). On the other hand, the risk of cancer-related mortality after radiation exposure is a stochastic matter. Epidemiological observations and biological experiments should be evaluated in tandem to strengthen conclusions about probabilistic causality ( box 1 ) .

While RCTs still retain their importance as the gold standard of clinical research, they cannot always be carried out. Some indispensable knowledge can only be obtained from observational studies. Confounding factors must be eliminated, or at least accounted for, early on when such studies are planned. Moreover, the data that are obtained must be carefully analyzed. And, finally, a single observational study hardly ever suffices to establish a causal relationship.

In this article, we have presented two newer methods that are relatively simple and which, therefore, could easily be used more widely in medical and epidemiological research ( 30 ). Either one should be used only after the prerequisites for its applicability have been meticulously checked. In regression-discontinuity methods, the assumption of continuity must be verified: in other words, it must be checked whether other properties of the treatment and control groups are the same, or at least equally balanced. The rules of group assignment and the role played by the continuous assignment variable must be known as well. Regression-discontinuity methods can generate causal conclusions, but any such conclusion will not be generalizable if the treatment effects are heterogeneous over the range of the assignment variable. The estimate of effect size is applicable only in a small, predefined interval around the threshold value. It must also be checked whether the outcome and the assignment variable are in a linear relationship, and whether there is any interaction between the treatment and assignment variables that needs to be considered.

In the analysis of interrupted time series, the assumption of continuity must be tested as well. Furthermore, the method is valid only if the occurrence of any other intervention at the same time point as the one under study can be ruled out ( 20 ). Finally, the type of temporal sequence must be considered, and more complex statistical methods must be applied, as needed, to take such phenomena as autoregression into account.

Observational studies often suggest causal relationships that will then be either supported or rejected after further studies and experiments. Knowledge of the effects of radiation exposure was derived, at first, mainly from observations on victims of the Hiroshima and Nagasaki atomic bomb explosions ( 31 ). These findings were reinforced by further epidemiological studies on other populations exposed to radiation (e.g., through medical procedures or as an occupational hazard), by physical considerations, and by biological experiments ( 32 ). A classic example from the mid-19 th century is the observational study by Snow ( 33 ): until then, the biological cause of cholera was unknown. Snow found that there had to be a causal relationship between the contamination of a well and a subsequent outbreak of cholera. This new understanding led to improved hygienic measures, which did, indeed, prevent infection with the cholera pathogen. Cases such as these prove that it is sometimes reasonable to take action on the basis of an observational study alone ( 6 ). They also demonstrate, however, that further studies are necessary for the definitive establishment of a causal relationship.

The effect of a smoking ban on the incidence of cardiovascular diseases

Key messages

• Causal inferences can be drawn from observational studies, as long as certain conditions are met.
• Confounding variables are a major impediment to the demonstration of causal links, as they can either obscure or mimic such a link.
• Random assignment leads to the even distribution of known and unknown confounders among the intervention groups that are being compared in the study.
• In the regression-discontinuity method, it is assumed that the assignment of patients to treatment groups is random with, in a small range of the assignment variable around the threshold, with the result that the confounders are randomly distributed as well.
• The interrupted time series is a variant of the regression-discontinuity method in which a given point in time splits the subjects into a before group and an after group, with random distribution of confounders to the two groups.

Acknowledgments

Translated from the original German by Ethan Taub, M.D.

Conflict of interest statement The authors state that they have no conflict of interest.

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.

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Understanding Methods for Research in Psychology

A Psychology Research Methods Study Guide

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

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

Types of Research in Psychology

• Cross-Sectional vs. Longitudinal Research
• Reliability and Validity

Glossary of Terms

Research in psychology focuses on a variety of topics , ranging from the development of infants to the behavior of social groups. Psychologists use the scientific method to investigate questions both systematically and empirically.

Research in psychology is important because it provides us with valuable information that helps to improve human lives. By learning more about the brain, cognition, behavior, and mental health conditions, researchers are able to solve real-world problems that affect our day-to-day lives.

At a Glance

Knowing more about how research in psychology is conducted can give you a better understanding of what those findings might mean to you. Psychology experiments can range from simple to complex, but there are some basic terms and concepts that all psychology students should understand.

Start your studies by learning more about the different types of research, the basics of experimental design, and the relationships between variables.

Research in Psychology: The Basics

The first step in your review should include a basic introduction to psychology research methods . Psychology research can have a variety of goals. What researchers learn can be used to describe, explain, predict, or change human behavior.

Psychologists use the scientific method to conduct studies and research in psychology. The basic process of conducting psychology research involves asking a question, designing a study, collecting data, analyzing results, reaching conclusions, and sharing the findings.

The Scientific Method in Psychology Research

The steps of the scientific method in psychology research are:

• Make an observation
• Ask a research question and make predictions about what you expect to find
• Test your hypothesis and gather data
• Examine the results and form conclusions
• Report your findings

Research in psychology can take several different forms. It can describe a phenomenon, explore the causes of a phenomenon, or look at relationships between one or more variables. Three of the main types of psychological research focus on:

Descriptive Studies

This type of research can tell us more about what is happening in a specific population. It relies on techniques such as observation, surveys, and case studies.

Correlational Studies

Correlational research is frequently used in psychology to look for relationships between variables. While research look at how variables are related, they do not manipulate any of the variables.

While correlational studies can suggest a relationship between two variables, finding a correlation does not prove that one variable causes a change in another. In other words, correlation does not equal causation.

Experimental Research Methods

Experiments are a research method that can look at whether changes in one variable cause changes in another. The simple experiment is one of the most basic methods of determining if there is a cause-and-effect relationship between two variables.

A simple experiment utilizes a control group of participants who receive no treatment and an experimental group of participants who receive the treatment.

Experimenters then compare the results of the two groups to determine if the treatment had an effect.

Cross-Sectional vs. Longitudinal Research in Psychology

Research in psychology can also involve collecting data at a single point in time, or gathering information at several points over a period of time.

Cross-Sectional Research

In a cross-sectional study , researchers collect data from participants at a single point in time. These are descriptive type of research and cannot be used to determine cause and effect because researchers do not manipulate the independent variables.

However, cross-sectional research does allow researchers to look at the characteristics of the population and explore relationships between different variables at a single point in time.

Longitudinal Research

A longitudinal study is a type of research in psychology that involves looking at the same group of participants over a period of time. Researchers start by collecting initial data that serves as a baseline, and then collect follow-up data at certain intervals. These studies can last days, months, or years. 

The longest longitudinal study in psychology was started in 1921 and the study is planned to continue until the last participant dies or withdraws. As of 2003, more than 200 of the partipants were still alive.

The Reliability and Validity of Research in Psychology

Reliability and validity are two concepts that are also critical in psychology research. In order to trust the results, we need to know if the findings are consistent (reliability) and that we are actually measuring what we think we are measuring (validity).

Reliability

Reliability is a vital component of a valid psychological test. What is reliability? How do we measure it? Simply put, reliability refers to the consistency of a measure. A test is considered reliable if we get the same result repeatedly.

When determining the merits of a psychological test, validity is one of the most important factors to consider. What exactly is validity? One of the greatest concerns when creating a psychological test is whether or not it actually measures what we think it is measuring.

For example, a test might be designed to measure a stable personality trait but instead measures transitory emotions generated by situational or environmental conditions. A valid test ensures that the results accurately reflect the dimension undergoing assessment.

Review some of the key terms that you should know and understand about psychology research methods. Spend some time studying these terms and definitions before your exam. Some key terms that you should know include:

• Correlation
• Demand characteristic
• Dependent variable
• Hawthorne effect
• Independent variable
• Naturalistic observation
• Placebo effect
• Random assignment
• Replication
• Selective attrition

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

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

Curtis EA, Comiskey C, Dempsey O. Importance and use of correlational research .  Nurse Res . 2016;23(6):20-25. doi:10.7748/nr.2016.e1382

Wang X, Cheng Z. Cross-sectional studies: Strengths, weaknesses, and recommendations .  Chest . 2020;158(1S):S65-S71. doi:10.1016/j.chest.2020.03.012

Caruana EJ, Roman M, Hernández-Sánchez J, Solli P. Longitudinal studies .  J Thorac Dis . 2015;7(11):E537-E540. doi:10.3978/j.issn.2072-1439.2015.10.63

Stanford Magazine. The vexing legacy of Lewis Terman .

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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• Cause and Effect

Establishing Cause and Effect

Cause and effect is one of the most commonly misunderstood concepts in science and is often misused by lawyers, the media, politicians and even scientists themselves, in an attempt to add legitimacy to research.

This article is a part of the guide:

• Experimental Research
• Pretest-Posttest
• Third Variable
• Research Bias
• Independent Variable

Browse Full Outline

• 1 Experimental Research
• 2.1 Independent Variable
• 2.2 Dependent Variable
• 2.3 Controlled Variables
• 2.4 Third Variable
• 3.1 Control Group
• 3.2 Research Bias
• 3.3.1 Placebo Effect
• 3.3.2 Double Blind Method
• 4.1 Randomized Controlled Trials
• 4.2 Pretest-Posttest
• 4.3 Solomon Four Group
• 4.4 Between Subjects
• 4.5 Within Subject
• 4.6 Repeated Measures
• 4.7 Counterbalanced Measures
• 4.8 Matched Subjects

The basic principle of causality is determining whether the results and trends seen in an experiment are actually caused by the manipulation or whether some other factor may underlie the process.

Unfortunately, the media and politicians often jump upon scientific results and proclaim that it conveniently fits their beliefs and policies. Some scientists, fixated upon 'proving' that their view of the world is correct, leak their results to the press before allowing the peer review process to check and validate their work.

Some examples of this are rife in alternative therapy, when a group of scientists announces that they have found the next healthy superfood or that a certain treatment cured swine flu. Many of these claims deviate from the scientific process and pay little heed to cause and effect, diluting the claims of genuine researchers in the field.

What is Cause and Effect? - The Temporal Issue

The key principle of establishing cause and effect is proving that the effects seen in the experiment happened after the cause.

This seems to be an extremely obvious statement, but that is not always the case. Natural phenomena are complicated and intertwined, often overlapping and making it difficult to establish a natural order. Think about it this way: in an experiment to study the effects of depression upon alcohol consumption, researchers find that people who suffer from higher levels of depression drink more, and announce that this correlation shows that depression drives people to drink.

However, is this necessarily the case? Depression could be the cause that makes people drink more but it is equally possible that heavy consumption of alcohol, a depressant, makes people more depressed. This type of classic 'chicken and egg' argument makes establishing causality one of the most difficult aspects of scientific research . It is also one of the most important factors, because it can misdirect scientists. It also leaves the research open to manipulation by interest groups, who will take the results and proclaim them as a truth.

With the above example, an alcoholic drink manufacturer could use the second interpretation to claim that alcohol is not a factor in depression and that the responsibility is upon society to ensure that people do not become depressed. An anti-alcohol group, on the other hand, could claim that alcohol is harmful and use the results to lobby for harsher drinking laws. The same research leads to two different interpretations and, the answer given to the media can depend upon who funds the work.

Unfortunately, most of the general public are not scientists and cannot be expected to filter every single news item that they read for quality or delve into which group funded research. Even respected and trusted newspapers, journals and internet resources can fall into the causality trap, so marketing groups can influence perceptions.

What is Cause and Effect? - The Danger of Alternative Explanations

The other problem with causality is that a researcher cannot always guarantee that their particular manipulation of a variable was the sole reason for the perceived trends and correlation.

In a complex experiment, it is often difficult to isolate and neutralize the influence of confounding variables . This makes it exceptionally difficult for the researcher to state that their treatment is the sole cause, so any research program must contain measures to establish the cause and effect relationship.

In the physical sciences, such as physics and chemistry, it is fairly easy to establish causality, because a good experimental design can neutralize any potentially confounding variables. Sociology, at the other extreme, is exceptionally prone to causality issues, because individual humans and social groups vary so wildly and are subjected to a wide range of external pressures and influences.

For results to have any meaning, a researcher must make causality the first priority, simply because it can have such a devastating effect upon validity. Most experiments with some validity issues can be salvaged, and produce some usable data. An experiment with no established cause and effect, on the other hand, will be practically useless and a waste of resources.

How to Establish Cause and Effect

The first thing to remember with causality, especially in the non-physical sciences, is that it is impossible to establish complete causality.

However, the magical figure of 100% proof of causality is what every researcher must strive for, to ensure that a group of their peers will accept the results. The only way to do this is through a strong and well-considered experimental design, often containing pilot studies to establish cause and effect before plowing on with a complex and expensive study.

The temporal factor is usually the easiest aspect to neutralize, simply because most experiments involve administering a treatment and then observing the effects, giving a linear temporal relationship. In experiments that use historical data, as with the drinking/depression example, this can be a little more complex. Most researchers performing such a program will supplement it with a series of individual case studies, and interviewing a selection of the participants , in depth, will allow the researchers to find the order of events.

For example, interviewing a sample of the depressed heavy drinkers will establish whether they felt that they were depressed before they started drinking or if the depression came later. The process of establishing cause and effect is a matter of ensuring that the potential influence of 'missing variables' is minimized.

One notable example, by the researchers Balnaves and Caputi, looked at the academic performance of university students and attempted to find a correlation with age. Indeed, they found that older, more mature students performed significantly better. However, as they pointed out, you cannot simply say that age causes the effect of making people into better students. Such a simplistic assumption is called a spurious relationship, the process of 'leaping to conclusions.'

In fact, there is a whole host of reasons why a mature student performs better: they have more life experience and confidence, and many feel that it is their last chance to succeed; my graduation year included a 75-year-old man, and nobody studied harder! Mature students may well have made a great financial sacrifice, so they are a little more determined to succeed. Establishing cause and effect is extremely difficult in this case, so the researchers interpreted the results very carefully.

Another example is the idea that because people who eat a lot of extra virgin olive oil live for longer, olive oil makes people live longer. While there is some truth behind this, you have to remember that most regular olive oil eaters also eat a Mediterranean diet, have active lifestyles, and generally less stress. These also have a strong influence, so any such research program should include studies into the effect of these - this is why a research program is not always a single experiment but often a series of experiments.

History Threats and Their Influence Upon Cause and Effect

One of the biggest threats to internal validity through incorrect application of cause and effect is the 'history' threat.

This is where another event actually caused the effect noticed, rather than your treatment or manipulation. Most researchers perform a pre-test upon a group, administer the treatment and then measure the post-test results ( pretest-posttest-design ). If the results are better, it is easy to assume that the treatment caused the result, but this is not necessarily the case.

For example, take the case of an educational researcher wishing to measure the effect of a new teaching method upon the mathematical aptitude of students. They pre-test, teach the new program for a few months and then posttest. Results improve, and they proclaim that their program works.

However, the research was ruined by a historical threat: during the course of the research, a major television network released a new educational series called 'Maths made Easy,' which most of the students watched. This influenced the results and compromised the validity of the experiment.

Fortunately, the solution to this problem is easy: if the researcher uses a two group pretest-posttest design with a control group , the control group will be equally influenced by the historical event, so the researcher can still establish a good baseline. There are a number of other 'single group' threats, but establishing a good control driven study largely eliminates these threats to causality.

Social Threats and Their Influence Upon Cause and Effect

Social threats are a big problem for social researchers simply because they are one of the most difficult of the threats to minimize. These types of threats arise from issues within the participant groups or the researchers themselves. In an educational setting, with two groups of children, one treated and one not, there are a number of potential issues.

• Diffusion or Imitation of Treatment: With this threat, information travels between groups and smoothes out any differences in the results. In a school, for example, students mix outside classes and may swap information or coach the control group about some of the great new study techniques that they have learned. It is practically impossible and extremely unfair to expect students not to mix, so this particular threat is always an issue.
• Compensatory Rivalry: Quite simply, this is where the control group becomes extremely jealous of the treatment group. They might think that the research is unfair, because their fellow students are earning better grades. As a result, they try much harder to show that they are equally as clever, reducing the difference between the two groups.
• Demoralization and Resentment: This jealousy may have the opposite effect and manifest as a built up resentment that the other group is receiving favorable treatment. The control group , quite simply, gives up and does not bother trying and their grades plummet. This makes the educational program appear to be much more successful than it really is.
• Compensatory Equalization of Treatment: This type of social threat arises from the attitude of the researchers or external contributors. If, for example, teachers and parents perceive that there is some unfairness in the system, they might try to compensate, by giving extra tuition or access to better teaching resources. This can easily cause compensatory rivalry, too, if a teacher spurs on the control group to try harder and outdo the others.

These social effects are extremely difficult to minimize without creating other threats to internal validity .

For example, using different schools is one idea, but this can lead to other internal validity issues, especially because the participant groups cannot be randomized. In reality, this is why most social research programs incorporate a variety of different methods and include more than one experiment, to establish the potential level of these threats and incorporate them into the interpretation of the data.

Cause and Effect - The Danger of Multiple Group Threats

Multiple group threats are a danger to causality caused by differences between two or more groups of participants. The main example of this is selection bias , or assignment bias, where the two groups are assigned unevenly, perhaps leaving one group with a larger proportion of high achievers. This will skew the results and mask the effects of the entire experiment.

While there are other types of multiple group threat, they are all subtypes of selection bias and involve the two groups receiving different treatment. If the groups are selected from different socio-economic backgrounds, or one has a much better teacher, this can skew the results. Without going into too much detail, the only way to reduce the influence of multiple group threats is through randomization , matched pairs designs or another assignment type.

As can be seen, establishing cause and effect is one of the most important factors in designing a robust research experiment. One of the best ways to learn about causality is through experience and analysis - every time you see some innovative research or findings in the media, think about what the results are trying to tell you and whether the researchers are justified in drawing their conclusions .

This does not have to be restricted to 'hard' science, because political researchers are the worst habitual offenders. Archaeology, economics and market research are other areas where cause and effect is important, so should provide some excellent examples of how to establish cause and effect.

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Martyn Shuttleworth (Sep 20, 2009). Establishing Cause and Effect. Retrieved Jun 26, 2024 from Explorable.com: https://explorable.com/cause-and-effect

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How to Use Surveys in Cause-Effect Research

Summary: to understand cause and effect, we need to conduct experiments. experiments may include surveys as a data collection method, but surveys in themselves can’t provide the answer..

3 minutes to read. By author Michaela Mora on July 29, 2019 Topics: Analysis Techniques , Market Research , Survey Design

Cause-effect research requires special research designs. However, many assume that surveys can uncover cause and effect links without much consideration.

Not long ago I got a call from a potential client asking for research to determine why a recent marketing campaign failed to increase sales, despite a significant increase in awareness.

He had conducted an advertising awareness survey, and the results showed that many in the target audience had noted the advertising and gave it high ratings, but didn’t make a purchase.

All possible explanations were mere speculations. He couldn’t pinpoint any particular cause for this. 

The main problem was that he looked for evidence of a cause-effect link, but the research design was not appropriate for that.

Cause-Effect Research=Experiment

The main method for cause-effect research is experimentation . In experimental-based research, we manipulate the causal or independent variables in a relatively controlled environment. This means that we control and monitor other variables affecting the dependent variable (e.g. sales) as much as possible.

In this case, the client had conducted the survey and analyzed the data without taking into account the effectiveness of different marketing collaterals, market penetration, competitor activity, and some characteristics of the purchase decision-makers.

After doing some digging around, we uncovered that in some markets, competitors had launched high-frequency advertising campaigns. This helped the client indirectly by increasing category awareness, but not his sales.

Moreover, the program targeted recent buyers who probably didn’t have a need for his products at that particular moment.

Marketing Experiments

Surveys that are not part of an experimental approach may show correlations, but not causality . 

To really connect the dots between cause and effect, we needed to create an experiment. This would include different renditions of the marketing collaterals, different markets, customers at different stages in the purchase cycle, and actions taken by competitors.

Experimentation in marketing has traditionally taken the form of standard test markets. In this approach, you launch controlled advertising in designated markets and sell the product through regular distribution channels.

However, these tests can be time-consuming, are often expensive, and may be difficult to administer.

Market Simulations

Simulated test markets are a more affordable solution. In this approach, we expose individuals to the product or concept (e.g. via actual marketing collaterals), and give them the opportunity to buy it. If they buy it, we ask them to evaluate the product and state their repeat purchase intent.

We can then combine trial and repeat estimates with data about promotions, distribution levels, competitor activity, and other relevant pieces of information.

User Research (UX)

Another experimentation channel is the popular freemium model , which mimics this process to some extent. The basic principle is to let people try it and observe what decision they make.

After this, we can follow up with research to understand what drove their decision while controlling for other variables that may affect the outcome. This approach goes deeper into user research .

Experiments are the Answer

In short, if you want to understand cause and effect, you need to conduct experiments.

Experiments may include surveys as a data collection method, but surveys in themselves can’t provide the answer. It is the experimental design that will lead you to it.

(An earlier version of this article was originally published on January 18, 2012. The article was last updated and revised on July 29, 2019.)

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Home » Research Methods – Types, Examples and Guide

Research Methods – Types, Examples and Guide

Table of Contents

Research Methods

Definition:

Research Methods refer to the techniques, procedures, and processes used by researchers to collect , analyze, and interpret data in order to answer research questions or test hypotheses. The methods used in research can vary depending on the research questions, the type of data that is being collected, and the research design.

Types of Research Methods

Types of Research Methods are as follows:

Qualitative research Method

Qualitative research methods are used to collect and analyze non-numerical data. This type of research is useful when the objective is to explore the meaning of phenomena, understand the experiences of individuals, or gain insights into complex social processes. Qualitative research methods include interviews, focus groups, ethnography, and content analysis.

Quantitative Research Method

Quantitative research methods are used to collect and analyze numerical data. This type of research is useful when the objective is to test a hypothesis, determine cause-and-effect relationships, and measure the prevalence of certain phenomena. Quantitative research methods include surveys, experiments, and secondary data analysis.

Mixed Method Research

Mixed Method Research refers to the combination of both qualitative and quantitative research methods in a single study. This approach aims to overcome the limitations of each individual method and to provide a more comprehensive understanding of the research topic. This approach allows researchers to gather both quantitative data, which is often used to test hypotheses and make generalizations about a population, and qualitative data, which provides a more in-depth understanding of the experiences and perspectives of individuals.

Key Differences Between Research Methods

The following Table shows the key differences between Quantitative, Qualitative and Mixed Research Methods

Examples of Research Methods

Examples of Research Methods are as follows:

Qualitative Research Example:

A researcher wants to study the experience of cancer patients during their treatment. They conduct in-depth interviews with patients to gather data on their emotional state, coping mechanisms, and support systems.

Quantitative Research Example:

A company wants to determine the effectiveness of a new advertisement campaign. They survey a large group of people, asking them to rate their awareness of the product and their likelihood of purchasing it.

Mixed Research Example:

A university wants to evaluate the effectiveness of a new teaching method in improving student performance. They collect both quantitative data (such as test scores) and qualitative data (such as feedback from students and teachers) to get a complete picture of the impact of the new method.

Applications of Research Methods

Research methods are used in various fields to investigate, analyze, and answer research questions. Here are some examples of how research methods are applied in different fields:

• Psychology : Research methods are widely used in psychology to study human behavior, emotions, and mental processes. For example, researchers may use experiments, surveys, and observational studies to understand how people behave in different situations, how they respond to different stimuli, and how their brains process information.
• Sociology : Sociologists use research methods to study social phenomena, such as social inequality, social change, and social relationships. Researchers may use surveys, interviews, and observational studies to collect data on social attitudes, beliefs, and behaviors.
• Medicine : Research methods are essential in medical research to study diseases, test new treatments, and evaluate their effectiveness. Researchers may use clinical trials, case studies, and laboratory experiments to collect data on the efficacy and safety of different medical treatments.
• Education : Research methods are used in education to understand how students learn, how teachers teach, and how educational policies affect student outcomes. Researchers may use surveys, experiments, and observational studies to collect data on student performance, teacher effectiveness, and educational programs.
• Business : Research methods are used in business to understand consumer behavior, market trends, and business strategies. Researchers may use surveys, focus groups, and observational studies to collect data on consumer preferences, market trends, and industry competition.
• Environmental science : Research methods are used in environmental science to study the natural world and its ecosystems. Researchers may use field studies, laboratory experiments, and observational studies to collect data on environmental factors, such as air and water quality, and the impact of human activities on the environment.
• Political science : Research methods are used in political science to study political systems, institutions, and behavior. Researchers may use surveys, experiments, and observational studies to collect data on political attitudes, voting behavior, and the impact of policies on society.

Purpose of Research Methods

Research methods serve several purposes, including:

• Identify research problems: Research methods are used to identify research problems or questions that need to be addressed through empirical investigation.
• Develop hypotheses: Research methods help researchers develop hypotheses, which are tentative explanations for the observed phenomenon or relationship.
• Collect data: Research methods enable researchers to collect data in a systematic and objective way, which is necessary to test hypotheses and draw meaningful conclusions.
• Analyze data: Research methods provide tools and techniques for analyzing data, such as statistical analysis, content analysis, and discourse analysis.
• Test hypotheses: Research methods allow researchers to test hypotheses by examining the relationships between variables in a systematic and controlled manner.
• Draw conclusions : Research methods facilitate the drawing of conclusions based on empirical evidence and help researchers make generalizations about a population based on their sample data.
• Enhance understanding: Research methods contribute to the development of knowledge and enhance our understanding of various phenomena and relationships, which can inform policy, practice, and theory.

When to Use Research Methods

Research methods are used when you need to gather information or data to answer a question or to gain insights into a particular phenomenon.

Here are some situations when research methods may be appropriate:

• To investigate a problem : Research methods can be used to investigate a problem or a research question in a particular field. This can help in identifying the root cause of the problem and developing solutions.
• To gather data: Research methods can be used to collect data on a particular subject. This can be done through surveys, interviews, observations, experiments, and more.
• To evaluate programs : Research methods can be used to evaluate the effectiveness of a program, intervention, or policy. This can help in determining whether the program is meeting its goals and objectives.
• To explore new areas : Research methods can be used to explore new areas of inquiry or to test new hypotheses. This can help in advancing knowledge in a particular field.
• To make informed decisions : Research methods can be used to gather information and data to support informed decision-making. This can be useful in various fields such as healthcare, business, and education.

Advantages of Research Methods

Research methods provide several advantages, including:

• Objectivity : Research methods enable researchers to gather data in a systematic and objective manner, minimizing personal biases and subjectivity. This leads to more reliable and valid results.
• Replicability : A key advantage of research methods is that they allow for replication of studies by other researchers. This helps to confirm the validity of the findings and ensures that the results are not specific to the particular research team.
• Generalizability : Research methods enable researchers to gather data from a representative sample of the population, allowing for generalizability of the findings to a larger population. This increases the external validity of the research.
• Precision : Research methods enable researchers to gather data using standardized procedures, ensuring that the data is accurate and precise. This allows researchers to make accurate predictions and draw meaningful conclusions.
• Efficiency : Research methods enable researchers to gather data efficiently, saving time and resources. This is especially important when studying large populations or complex phenomena.
• Innovation : Research methods enable researchers to develop new techniques and tools for data collection and analysis, leading to innovation and advancement in the field.

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Causal Analysis in Research: Types of Casual Analysis

Causal analysis is a research technique that can help businesses get to the root of specific behaviors or events . It’s like detective work, where you try to figure out what caused something to happen. By proving a connection between cause and effect of two variables, this method can assist in evaluating marketing campaigns, improving internal processes, and achieving successful business goals.

Curious how you can conduct causal analysis yourself? Well, you’ve come to the right place. In this article, we’ll explore causal research in-depth and cover all its essential components, advantages, examples, and critical tips. Whether you’re a business owner or a researcher, understanding the fundamentals of causal analysis is crucial for making informed decisions based on reliable evidence. So, without any further ado, let’s dive in and learn more!

Understanding Causality in Research – Importance of Causal Analysis

Causal analysis, also known as explanatory research , helps researchers establish a cause-and-effect link between two variables. It’s a commonly used method in research and statistics . The terms cause and effect are essential to this concept. Cause means something that creates a scenario, and effect refers to what happens as a result of that cause. In causal analysis, a causal effect is what has occurred or is occurring as a consequence of an event that has happened or is happening.

To obtain accurate results while conducting causal analysis, researchers must keep other confounding variables constant while creating or controlling data. However, this can be complicated as other hidden variables may influence the causal relationship.

The primary objective of causal analysis is to identify the root cause of a problem to develop effective solutions. It’s an essential tool for decision-making in various fields, including medicine, psychology, economics, and social sciences.

For example, a company conducting a study on consumer behavior toward changing prices of goods may only be partially confident of the results due to the possible influence of psychological factors on the customer’s decision-making process. Causal analysis helps to account for such factors and provide a more accurate understanding of the relationship between the variables.

Types of Causal Analysis

While working with a research study, causal analysis can be a powerful tool for understanding the relationships (cause and effect) between different variables. Let’s discuss the various types of causal analysis and how they can be used in research:

Experimental research is a valuable technique for conducting causal analysis as it involves altering one variable while closely observing its impact on another variable in a controlled setting. Using statistical data and quantitative research methodologies, researchers can use this method to determine a cause-and-effect connection between the two variables.

Experimental research is particularly effective in identifying  time-sensitive causal relationships  and understanding the significance of causality. It can also provide valuable insights for decision-making. Therefore, conducting experimental studies is highly recommended to understand better how different variables interact.

A   quasi-experimental design is a research method that shares similarities with experimental research but with a crucial difference – the lack of a control group. In this study, researchers manipulate an independent variable, but the group of participants is not randomly assigned. Quasi-research is typically conducted in real-world settings where random assignment may not be possible or necessary.

This method can be beneficial when manipulating the independent variable is neither ethical nor practical. Although quasi-experimental design lacks complete control over variables, it can provide valuable insights into causal relationships in certain situations. So, suppose you are conducting research in a field setting where random assignment is not feasible or relevant. In that case, a quasi-experimental design could be a practical approach to exploring causal relationships.

Observational research involves observing and measuring variables without manipulation and is useful when experiments are not possible or ethical. Researchers use surveys, questionnaires, or interviews to record behaviors, events, or phenomena in natural or controlled settings.

However, like other observational studies, case-control studies cannot establish causation independently and are subject to potential bias. Therefore, they are typically used with other studies, such as cohort studies or randomized controlled trials, to provide more substantial evidence for causation.

How is Causal Analysis Different from Other Research Methods?

Causal analysis is unique in its focus on determining cause-and-effect relationships between variables, setting it apart from other research methods like descriptive and exploratory research which aim to describe or explore phenomena . Causal analysis involves manipulating an independent variable and assessing its effects on a dependent variable in a controlled setting, typically a laboratory, and using statistical data analysis to draw conclusions. In contrast, other research methods may rely on surveys, interviews, or observations to gather data.

Causal analysis is particularly useful in comprehending complexities and making predictions as it identifies the factors contributing to a particular outcome. Overall, the emphasis on causality and the use of experimental research distinguishes causal analysis as a distinct research method.

What Are the Three Criteria for Causality?

The importance of data analysis in research, is data analysis qualitative or quantitative (we find out), causal analysis – pros and cons, steps for conducting causal analysis.

Step 2: Enter the causal research phase utilizing insights from exploratory, descriptive, and correlational research.

Step 4: Establish temporal sequence, and concomitant variation, and eliminate spurious correlations.

Step 5: Experiment in a controlled environment by determining the effect on a dependent variable caused by manipulating the independent variable.

Causal analysis has demonstrated its usefulness in various fields and has been practically applied in many ways. Here are some examples of how causal analysis has been implemented:

On the launching of a new product, tracking its sales and analyzing customer feedback allows the company to rank the launch’s success, hence establishing a cause-and-effect relationship. This can result in valuable statistics for the company to decide whether to continue promoting the product or make changes to the product or marketing strategy for future launches.

Clinical Trials to assess the efficacy of a new drug:

For the treatment of a particular disease, clinical trials are held to test the potency of a new product. Patients are randomly assigned either the concerned drug or a placebo, and the outcomes between the two groups are compared. The findings of this analysis can determine whether the drug is effective, hence a perfect example of causal analysis.

Companies can identify areas for improvement in their products or services by analyzing customer feedback. This may entail gathering and analyzing information from surveys, social media, or customer service interactions. This analysis’s findings can be used to increase customer satisfaction and loyalty.

Improving student learning outcomes:

What sets causal analysis apart from other research methods is its primary focus on establishing causality. Unlike other research methods, such as descriptive or correlational research, which can only identify relationships between variables, causal analysis involves manipulating one variable to observe its impact on another variable and ruling out alternative explanations. This approach allows researchers to determine the root cause of a problem and develop practical solutions based on reliable evidence.

Lastly, here’s a take-home summary table for you to quickly differentiate between causal analysis and other research methodologies:

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Establishing a Cause-Effect Relationship

How do we establish a cause-effect (causal) relationship? What criteria do we have to meet? Generally, there are three criteria that you must meet before you can say that you have evidence for a causal relationship:

Temporal Precedence

First, you have to be able to show that your cause happened before your effect. Sounds easy, huh? Of course my cause has to happen before the effect. Did you ever hear of an effect happening before its cause? Before we get lost in the logic here, consider a classic example from economics: does inflation cause unemployment? It certainly seems plausible that as inflation increases, more employers find that in order to meet costs they have to lay off employees. So it seems that inflation could, at least partially, be a cause for unemployment. But both inflation and employment rates are occurring together on an ongoing basis. Is it possible that fluctuations in employment can affect inflation? If we have an increase in the work force (i.e. lower unemployment) we may have more demand for goods, which would tend to drive up the prices (i.e. inflate them) at least until supply can catch up. So which is the cause and which the effect, inflation or unemployment? It turns out that in this kind of cyclical situation involving ongoing processes that interact that both may cause and, in turn, be affected by the other. This makes it very hard to establish a causal relationship in this situation.

Covariation of the Cause and Effect

What does this mean? Before you can show that you have a causal relationship you have to show that you have some type of relationship. For instance, consider the syllogism:

if X then Y if not X then not Y

If you observe that whenever X is present, Y is also present, and whenever X is absent, Y is too, then you have demonstrated that there is a relationship between X and Y. I don’t know about you, but sometimes I find it’s not easy to think about X’s and Y’s. Let’s put this same syllogism in program evaluation terms:

if program then outcome if not program then not outcome

Or, in colloquial terms: if you give a program you observe the outcome but if you don’t give the program you don’t observe the outcome. This provides evidence that the program and outcome are related. Notice, however, that this syllogism doesn’t not provide evidence that the program caused the outcome — perhaps there was some other factor present with the program that caused the outcome, rather than the program. The relationships described so far are rather simple binary relationships. Sometimes we want to know whether different amounts of the program lead to different amounts of the outcome — a continuous relationship:

if more of the program then more of the outcome if less of the program then less of the outcome

No Plausible Alternative Explanations

Just because you show there’s a relationship doesn’t mean it’s a causal one. It’s possible that there is some other variable or factor that is causing the outcome. This is sometimes referred to as the “third variable” or “missing variable” problem and it’s at the heart of the issue of internal validity. What are some of the possible plausible alternative explanations? Just go look at the threats to internal validity (see single group threats , multiple group threats or social threats ) — each one describes a type of alternative explanation.

In order for you to argue that you have demonstrated internal validity — that you have shown there’s a causal relationship — you have to “rule out” the plausible alternative explanations. How do you do that? One of the major ways is with your research design. Let’s consider a simple single group threat to internal validity, a history threat. Let’s assume you measure your program group before they start the program (to establish a baseline), you give them the program, and then you measure their performance afterwards in a posttest. You see a marked improvement in their performance which you would like to infer is caused by your program. One of the plausible alternative explanations is that you have a history threat — it’s not your program that caused the gain but some other specific historical event. For instance, it’s not your anti-smoking campaign that caused the reduction in smoking but rather the Surgeon General’s latest report that happened to be issued between the time you gave your pretest and posttest. How do you rule this out with your research design? One of the simplest ways would be to incorporate the use of a control group — a group that is comparable to your program group with the only difference being that they didn’t receive the program. But they did experience the Surgeon General’s latest report. If you find that they didn’t show a reduction in smoking even though they did experience the same Surgeon General report you have effectively “ruled out” the Surgeon General’s report as a plausible alternative explanation for why you observed the smoking reduction.

In most applied social research that involves evaluating programs, temporal precedence is not a difficult criterion to meet because you administer the program before you measure effects. And, establishing covariation is relatively simple because you have some control over the program and can set things up so that you have some people who get it and some who don’t (if X and if not X). Typically the most difficult criterion to meet is the third —ruling out alternative explanations for the observed effect. That is why research design is such an important issue and why it is intimately linked to the idea of internal validity.

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1.14: Cause and Effect

Chapter 1: research methods, chapter 2: the social self, chapter 3: social judgement and decision-making, chapter 4: understanding and influencing others, chapter 5: attitudes and persuasion, chapter 6: close relationships, chapter 7: stereotypes, prejudice, and discrimination, chapter 8: helping and hurting, chapter 9: group dynamics.

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In some cases, the relationship between two items seems clear and intuitive, like when there’s a sports game, people will order pizza even though it’s getting late. One member of the group swears that eating several slices before retiring for the evening gives him nightmares.

Now, the only way to answer this question "Does eating pizza right before going to sleep cause someone to have more nightmares?" is to design an experiment.

In one type of experimental design, a cause-and-effect relationship, a researcher can determine whether manipulating an independent variable—in this case, eating pizza before bedtime—causes a particular effect—changes in the dependent variable, the number of nightmares that occur throughout the night.

They could assign half of the participants to the experimental group, which is given the experimental manipulation—the assignment of eating three slices of pizza right before they go to bed—and the second half to the control group, which is instructed not to eat anything.

They can also take certain measures to control for confounding variables that may produce alternative explanations.

For example, by randomly assigning participants to different groups—using a probability-based method—the researcher can ensure that participants are equally matched on potential confounds, for instance, their history of and predisposition for nightmares, the time it takes them to fall asleep, and even their overall quality of sleep.

In addition, the researcher could conduct the experiment in a laboratory setting where even more factors can be controlled for, like the sleep environment and what they watch before sleeping.

In the end, the only way to establish causality between two variables is by running sound experiments!

While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?

There is no question that a relationship exists between ice cream and crime ( e.g. , Harper, 2013), but it would be pretty foolish to decide that one thing actually caused the other to occur. It is much more likely that both ice cream sales and crime rates are related to the temperature outside. In this case, temperature is a confounding variable that could account for the relationship between the two variables. When the temperature is warm, there are lots of people out of their houses, interacting with each other, getting annoyed with one another, and sometimes committing crimes. Also, when it is warm outside, we are more likely to seek a cool treat like ice cream. How do we determine if there is indeed a relationship between two things? And when there is a relationship, how can we discern whether it is attributable to coincidence or causation?

Even when we cannot point to clear confounding variables, we should not assume that a correlation between two variables implies that one variable causes changes in another. This can be frustrating when a cause-and-effect relationship seems clear and intuitive. Think about the American Cancer Society and how their research projects were some of the first demonstrations of the link between smoking and cancer. It seems reasonable to assume that smoking causes cancer, but if we were limited to correlational research, we would be overstepping our bounds by making this assumption.

Unfortunately, people mistakenly make claims of causation as a function of correlations all the time. Such claims are especially common in advertisements and news stories. For example, recent research found that people who eat cereal on a regular basis achieve healthier weights than those who rarely eat cereal (Frantzen, Treviño, Echon, Garcia-Dominic, & DiMarco, 2013; Barton et al ., 2005). Guess how the cereal companies report this finding. Does eating cereal really cause an individual to maintain a healthy weight, or are there other possible explanations, such as, someone at a healthy weight is more likely to regularly eat a healthy breakfast than someone who is obese or someone who avoids meals in an attempt to diet? While correlational research is invaluable in identifying relationships among variables, a major limitation is the inability to establish causality. Psychologists want to make statements about cause and effect, but the only way to do that is to conduct an experiment to answer a research question. Scientific experiments incorporate methods that eliminate, or control for, alternative explanations, which allow researchers to explore how changes in one variable cause changes in another variable.

Illusory Correlations

The temptation to make erroneous cause-and-effect statements based on correlational research is not the only way we tend to misinterpret data. We also tend to make the mistake of illusory correlations, especially with unsystematic observations. Illusory correlations, or false correlations, occur when people believe that relationships exist between two things when no such relationship exists. One well-known illusory correlation is the supposed effect that the moon’s phases have on human behavior. Many people passionately assert that human behavior is affected by the phase of the moon, and specifically, that people act strangely when the moon is full.

There is no denying that the moon exerts a powerful influence on our planet. The ebb and flow of the ocean’s tides are tightly tied to the gravitational forces of the moon. Many people believe, therefore, that it is logical that we are affected by the moon as well. After all, our bodies are largely made up of water. A meta-analysis of nearly 40 studies consistently demonstrated, however, that the relationship between the moon and our behavior does not exist (Rotton & Kelly, 1985). While we may pay more attention to odd behavior during the full phase of the moon, the rates of odd behavior remain constant throughout the lunar cycle.

Why are we so apt to believe in illusory correlations like this? Often we read or hear about them and simply accept the information as valid. Or, we have a hunch about how something works and then look for evidence to support that hunch, ignoring evidence that would tell us our hunch is false; this is known as confirmation bias. Other times, we find illusory correlations based on the information that comes most easily to mind, even if that information is severely limited. And while we may feel confident that we can use these relationships to better understand and predict the world around us, illusory correlations can have significant drawbacks. For example, research suggests that illusory correlations—in which certain behaviors are inaccurately attributed to certain groups—are involved in the formation of prejudicial attitudes that can ultimately lead to discriminatory behavior (Fiedler, 2004).

This text is adapted from OpenStax, Psychology. OpenStax CNX.

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3 Causes-of-Effects versus Effects-of-Causes

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This chapter examines two approaches used in social science research: the “causes-of-effects” approach and the “effects-of-causes” approach. The quantitative and qualitative cultures differ in the extent to which and the ways in which they address causes-of-effects and effects-of-causes questions. Quantitative scholars, who favor the effects-of-causes approach, focus on estimating the average effects of particular variables within populations or samples. By contrast, qualitative scholars employ individual case analysis to explain outcomes as well as the effects of particular causal factors. The chapter first considers the type of research question addressed by both quantitative and qualitative researchers before discussing the use of within-case analysis by the latter to investigate individual cases versus cross-case analysis by the former to elucidate central tendencies in populations. It also describes the complementarities between qualitative and quantitative research that make mixed-method research possible.

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A step-by-step guide to causal study design using real-world data

• Open access
• Published: 19 June 2024

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• Sarah Ruth Hoffman 1 ,
• Nilesh Gangan 1 ,
• Xiaoxue Chen 2 ,
• Joseph L. Smith 1 ,
• Arlene Tave 1 ,
• Yiling Yang 1 ,
• Christopher L. Crowe 1 ,
• Susan dosReis 3 &
• Michael Grabner 1  

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Due to the need for generalizable and rapidly delivered evidence to inform healthcare decision-making, real-world data have grown increasingly important to answer causal questions. However, causal inference using observational data poses numerous challenges, and relevant methodological literature is vast. We endeavored to identify underlying unifying themes of causal inference using real-world healthcare data and connect them into a single schema to aid in observational study design, and to demonstrate this schema using a previously published research example. A multidisciplinary team (epidemiology, biostatistics, health economics) reviewed the literature related to causal inference and observational data to identify key concepts. A visual guide to causal study design was developed to concisely and clearly illustrate how the concepts are conceptually related to one another. A case study was selected to demonstrate an application of the guide. An eight-step guide to causal study design was created, integrating essential concepts from the literature, anchored into conceptual groupings according to natural steps in the study design process. The steps include defining the causal research question and the estimand; creating a directed acyclic graph; identifying biases and design and analytic techniques to mitigate their effect, and techniques to examine the robustness of findings. The cardiovascular case study demonstrates the applicability of the steps to developing a research plan. This paper used an existing study to demonstrate the relevance of the guide. We encourage researchers to incorporate this guide at the study design stage in order to elevate the quality of future real-world evidence.

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1 Introduction

Approximately 50 new drugs are approved each year in the United States (Mullard 2022 ). For all new drugs, randomized controlled trials (RCTs) are the gold-standard by which potential effectiveness (“efficacy”) and safety are established. However, RCTs cannot guarantee how a drug will perform in a less controlled context. For this reason, regulators frequently require observational, post-approval studies using “real-world” data, sometimes even as a condition of drug approval. The “real-world” data requested by regulators is often derived from insurance claims databases and/or healthcare records. Importantly, these data are recorded during routine clinical care without concern for potential use in research. Yet, in recent years, there has been increasing use of such data for causal inference and regulatory decision making, presenting a variety of methodologic challenges for researchers and stakeholders to consider (Arlett et al. 2022 ; Berger et al. 2017 ; Concato and ElZarrad 2022 ; Cox et al. 2009 ; European Medicines Agency 2023 ; Franklin and Schneeweiss 2017 ; Girman et al. 2014 ; Hernán and Robins 2016 ; International Society for Pharmacoeconomics and Outcomes Research (ISPOR) 2022 ; International Society for Pharmacoepidemiology (ISPE) 2020 ; Stuart et al. 2013 ; U.S. Food and Drug Administration 2018 ; Velentgas et al. 2013 ).

Current guidance for causal inference using observational healthcare data articulates the need for careful study design (Berger et al. 2017 ; Cox et al. 2009 ; European Medicines Agency 2023 ; Girman et al. 2014 ; Hernán and Robins 2016 ; Stuart et al. 2013 ; Velentgas et al. 2013 ). In 2009, Cox et al. described common sources of bias in observational data and recommended specific strategies to mitigate these biases (Cox et al. 2009 ). In 2013, Stuart et al. emphasized counterfactual theory and trial emulation, offered several approaches to address unmeasured confounding, and provided guidance on the use of propensity scores to balance confounding covariates (Stuart et al. 2013 ). In 2013, the Agency for Healthcare Research and Quality (AHRQ) released an extensive, 200-page guide to developing a protocol for comparative effectiveness research using observational data (Velentgas et al. 2013 ). The guide emphasized development of the research question, with additional chapters on study design, comparator selection, sensitivity analyses, and directed acyclic graphs (Velentgas et al. 2013 ). In 2014, Girman et al. provided a clear set of steps for assessing study feasibility including examination of the appropriateness of the data for the research question (i.e., ‘fit-for-purpose’), empirical equipoise, and interpretability, stating that comparative effectiveness research using observational data “should be designed with the goal of drawing a causal inference” (Girman et al. 2014 ). In 2017 , Berger et al. described aspects of “study hygiene,” focusing on procedural practices to enhance confidence in, and credibility of, real-world data studies (Berger et al. 2017 ). Currently, the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP) maintains a guide on methodological standards in pharmacoepidemiology which discusses causal inference using observational data and includes an overview of study designs, a chapter on methods to address bias and confounding, and guidance on writing statistical analysis plans (European Medicines Agency 2023 ). In addition to these resources, the “target trial framework” provides a structured approach to planning studies for causal inferences from observational databases (Hernán and Robins 2016 ; Wang et al. 2023b ). This framework, published in 2016, encourages researchers to first imagine a clinical trial for the study question of interest and then to subsequently design the observational study to reflect the hypothetical trial (Hernán and Robins 2016 ).

While the literature addresses critical issues collectively, there remains a need for a framework that puts key components, including the target trial approach, into a simple, overarching schema (Loveless 2022 ) so they can be more easily remembered, and communicated to all stakeholders including (new) researchers, peer-reviewers, and other users of the research findings (e.g., practicing providers, professional clinical societies, regulators). For this reason, we created a step-by-step guide for causal inference using administrative health data, which aims to integrate these various best practices at a high level and complements existing, more specific guidance, including those from the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) and the International Society for Pharmacoepidemiology (ISPE) (Berger et al. 2017 ; Cox et al. 2009 ; Girman et al. 2014 ). We demonstrate the application of this schema using a previously published paper in cardiovascular research.

This work involved a formative phase and an implementation phase to evaluate the utility of the causal guide. In the formative phase, a multidisciplinary team with research expertise in epidemiology, biostatistics, and health economics reviewed selected literature (peer-reviewed publications, including those mentioned in the introduction, as well as graduate-level textbooks) related to causal inference and observational healthcare data from the pharmacoepidemiologic and pharmacoeconomic perspectives. The potential outcomes framework served as the foundation for our conception of causal inference (Rubin 2005 ). Information was grouped into the following four concepts: (1) Defining the Research Question; (2) Defining the Estimand; (3) Identifying and Mitigating Biases; (4) Sensitivity Analysis. A step-by-step guide to causal study design was developed to distill the essential elements of each concept, organizing them into a single schema so that the concepts are clearly related to one another. References for each step of the schema are included in the Supplemental Table.

In the implementation phase we tested the application of the causal guide to previously published work (Dondo et al. 2017 ). The previously published work utilized data from the Myocardial Ischaemia National Audit Project (MINAP), the United Kingdom’s national heart attack register. The goal of the study was to assess the effect of β-blockers on all-cause mortality among patients hospitalized for acute myocardial infarction without heart failure or left ventricular systolic dysfunction. We selected this paper for the case study because of its clear descriptions of the research goal and methods, and the explicit and methodical consideration of potential biases and use of sensitivity analyses to examine the robustness of the main findings.

3.1 Overview of the eight steps

The step-by-step guide to causal inference comprises eight distinct steps (Fig.  1 ) across the four concepts. As scientific inquiry and study design are iterative processes, the various steps may be completed in a different order than shown, and steps may be revisited.

A step-by-step guide for causal study design

Abbreviations: GEE: generalized estimating equations; IPC/TW: inverse probability of censoring/treatment weighting; ITR: individual treatment response; MSM: marginal structural model; TE: treatment effect

Please refer to the Supplemental Table for references providing more in-depth information.

1 Ensure that the exposure and outcome are well-defined based on literature and expert opinion.

2 More specifically, measures of association are not affected by issues such as confounding and selection bias because they do not intend to isolate and quantify a single causal pathway. However, information bias (e.g., variable misclassification) can negatively affect association estimates, and association estimates remain subject to random variability (and are hence reported with confidence intervals).

3 This list is not exhaustive; it focuses on frequently encountered biases.

4 To assess bias in a nonrandomized study following the target trial framework, use of the ROBINS-I tool is recommended ( https://www.bmj.com/content/355/bmj.i4919 ).

5 Only a selection of the most popular approaches is presented here. Other methods exist; e.g., g-computation and g-estimation for both time-invariant and time-varying analysis; instrumental variables; and doubly-robust estimation methods. There are also program evaluation methods (e.g., difference-in-differences, regression discontinuities) that can be applied to pharmacoepidemiologic questions. Conventional outcome regression analysis is not recommended for causal estimation due to issues determining covariate balance, correct model specification, and interpretability of effect estimates.

6 Online tools include, among others, an E-value calculator for unmeasured confounding ( https://www.evalue-calculator.com /) and the P95 outcome misclassification estimator ( http://apps.p-95.com/ISPE /).

3.2 Defining the Research question (step 1)

The process of designing a study begins with defining the research question. Research questions typically center on whether a causal relationship exists between an exposure and an outcome. This contrasts with associative questions, which, by their nature, do not require causal study design elements because they do not attempt to isolate a causal pathway from a single exposure to an outcome under study. It is important to note that the phrasing of the question itself should clarify whether an association or a causal relationship is of interest. The study question “Does statin use reduce the risk of future cardiovascular events?” is explicitly causal and requires that the study design addresses biases such as confounding. In contrast, the study question “Is statin use associated with a reduced risk of future cardiovascular events?” can be answered without control of confounding since the word “association” implies correlation. Too often, however, researchers use the word “association” to describe their findings when their methods were created to address explicitly causal questions (Hernán 2018 ). For example, a study that uses propensity score-based methods to balance risk factors between treatment groups is explicitly attempting to isolate a causal pathway by removing confounding factors. This is different from a study that intends only to measure an association. In fact, some journals may require that the word “association” be used when causal language would be more appropriate; however, this is beginning to change (Flanagin et al. 2024 ).

3.3 Defining the estimand (steps 2, 3, 4)

The estimand is the causal effect of research interest and is described in terms of required design elements: the target population for the counterfactual contrast, the kind of effect, and the effect/outcome measure.

In Step 2, the study team determines the target population of interest, which depends on the research question of interest. For example, we may want to estimate the effect of the treatment in the entire study population, i.e., the hypothetical contrast between all study patients taking the drug of interest versus all study patients taking the comparator (the average treatment effect; ATE). Other effects can be examined, including the average treatment effect in the treated or untreated (ATT or ATU).When covariate distributions are the same across the treated and untreated populations and there is no effect modification by covariates, these effects are generally the same (Wang et al. 2017 ). In RCTs, this occurs naturally due to randomization, but in non-randomized data, careful study design and statistical methods must be used to mitigate confounding bias.

In Step 3, the study team decides whether to measure the intention-to-treat (ITT), per-protocol, or as-treated effect. The ITT approach is also known as “first-treatment-carried-forward” in the observational literature (Lund et al. 2015 ). In trials, the ITT measures the effect of treatment assignment rather than the treatment itself, and in observational data the ITT can be conceptualized as measuring the effect of treatment as started . To compute the ITT effect from observational data, patients are placed into the exposure group corresponding to the treatment that they initiate, and treatment switching or discontinuation are purposely ignored in the analysis. Alternatively, a per-protocol effect can be measured from observational data by classifying patients according to the treatment that they initiated but censoring them when they stop, switch, or otherwise change treatment (Danaei et al. 2013 ; Yang et al. 2014 ). Finally, “as-treated” effects are estimated from observational data by classifying patients according to their actual treatment exposure during follow-up, for example by using multiple time windows to measure exposure changes (Danaei et al. 2013 ; Yang et al. 2014 ).

Step 4 is the final step in specifying the estimand in which the research team determines the effect measure of interest. Answering this question has two parts. First, the team must consider how the outcome of interest will be measured. Risks, rates, hazards, odds, and costs are common ways of measuring outcomes, but each measure may be best suited to a particular scenario. For example, risks assume patients across comparison groups have equal follow-up time, while rates allow for variable follow-up time (Rothman et al. 2008 ). Costs may be of interest in studies focused on economic outcomes, including as inputs to cost-effectiveness analyses. After deciding how the outcome will be measured, it is necessary to consider whether the resulting quantity will be compared across groups using a ratio or a difference. Ratios convey the effect of exposure in a way that is easy to understand, but they do not provide an estimate of how many patients will be affected. On the other hand, differences provide a clearer estimate of the potential public health impact of exposure; for example, by allowing the calculation of the number of patients that must be treated to cause or prevent one instance of the outcome of interest (Tripepi et al. 2007 ).

3.4 Identifying and mitigating biases (steps 5, 6, 7)

Observational, real-world studies can be subject to multiple potential sources of bias, which can be grouped into confounding, selection, measurement, and time-related biases (Prada-Ramallal et al. 2019 ).

In Step 5, as a practical first approach in developing strategies to address threats to causal inference, researchers should create a visual mapping of factors that may be related to the exposure, outcome, or both (also called a directed acyclic graph or DAG) (Pearl 1995 ). While creating a high-quality DAG can be challenging, guidance is increasingly available to facilitate the process (Ferguson et al. 2020 ; Gatto et al. 2022 ; Hernán and Robins 2020 ; Rodrigues et al. 2022 ; Sauer 2013 ). The types of inter-variable relationships depicted by DAGs include confounders, colliders, and mediators. Confounders are variables that affect both exposure and outcome, and it is necessary to control for them in order to isolate the causal pathway of interest. Colliders represent variables affected by two other variables, such as exposure and outcome (Griffith et al. 2020 ). Colliders should not be conditioned on since by doing so, the association between exposure and outcome will become distorted. Mediators are variables that are affected by the exposure and go on to affect the outcome. As such, mediators are on the causal pathway between exposure and outcome and should also not be conditioned on, otherwise a path between exposure and outcome will be closed and the total effect of the exposure on the outcome cannot be estimated. Mediation analysis is a separate type of analysis aiming to distinguish between direct and indirect (mediated) effects between exposure and outcome and may be applied in certain cases (Richiardi et al. 2013 ). Overall, the process of creating a DAG can create valuable insights about the nature of the hypothesized underlying data generating process and the biases that are likely to be encountered (Digitale et al. 2022 ). Finally, an extension to DAGs which incorporates counterfactual theory is available in the form of Single World Intervention Graphs (SWIGs) as described in a 2013 primer (Richardson and Robins 2013 ).

In Step 6, researchers comprehensively assess the possibility of different types of bias in their study, above and beyond what the creation of the DAG reveals. Many potential biases have been identified and summarized in the literature (Berger et al. 2017 ; Cox et al. 2009 ; European Medicines Agency 2023 ; Girman et al. 2014 ; Stuart et al. 2013 ; Velentgas et al. 2013 ). Every study can be subject to one or more biases, each of which can be addressed using one or more methods. The study team should thoroughly and explicitly identify all possible biases with consideration for the specifics of the available data and the nuances of the population and health care system(s) from which the data arise. Once the potential biases are identified and listed, the team can consider potential solutions using a variety of study design and analytic techniques.

In Step 7, the study team considers solutions to the biases identified in Step 6. “Target trial” thinking serves as the basis for many of these solutions by requiring researchers to consider how observational studies can be designed to ensure comparison groups are similar and produce valid inferences by emulating RCTs (Labrecque and Swanson 2017 ; Wang et al. 2023b ). Designing studies to include only new users of a drug and an active comparator group is one way of increasing the similarity of patients across both groups, particularly in terms of treatment history. Careful consideration must be paid to the specification of the time periods and their relationship to inclusion/exclusion criteria (Suissa and Dell’Aniello 2020 ). For instance, if a drug is used intermittently, a longer wash-out period is needed to ensure adequate capture of prior use in order to avoid bias (Riis et al. 2015 ). The study team should consider how to approach confounding adjustment, and whether both time-invariant and time-varying confounding may be present. Many potential biases exist, and many methods have been developed to address them in order to improve causal estimation from observational data. Many of these methods, such as propensity score estimation, can be enhanced by machine learning (Athey and Imbens 2019 ; Belthangady et al. 2021 ; Mai et al. 2022 ; Onasanya et al. 2024 ; Schuler and Rose 2017 ; Westreich et al. 2010 ). Machine learning has many potential applications in the causal inference discipline, and like other tools, must be used with careful planning and intentionality. To aid in the assessment of potential biases, especially time-related ones, and the development of a plan to address them, the study design should be visualized (Gatto et al. 2022 ; Schneeweiss et al. 2019 ). Additionally, we note the opportunity for collaboration across research disciplines (e.g., the application of difference-in-difference methods (Zhou et al. 2016 ) to the estimation of comparative drug effectiveness and safety).

3.5 Quality Control & sensitivity analyses (step 8)

Causal study design concludes with Step 8, which includes planning quality control and sensitivity analyses to improve the internal validity of the study. Quality control begins with reviewing study output for prima facie validity. Patient characteristics (e.g., distributions of age, sex, region) should align with expected values from the researchers’ intuition and the literature, and researchers should assess reasons for any discrepancies. Sensitivity analyses should be conducted to determine the robustness of study findings. Researchers can test the stability of study estimates using a different estimand or type of model than was used in the primary analysis. Sensitivity analysis estimates that are similar to those of the primary analysis might confirm that the primary analysis estimates are appropriate. The research team may be interested in how changes to study inclusion/exclusion criteria may affect study findings or wish to address uncertainties related to measuring the exposure or outcome in the administrative data by modifying the algorithms used to identify exposure or outcome (e.g., requiring hospitalization with a diagnosis code in a principal position rather than counting any claim with the diagnosis code in any position). As feasible, existing validation studies for the exposure and outcome should be referenced, or new validation efforts undertaken. The results of such validation studies can inform study estimates via quantitative bias analyses (Lanes and Beachler 2023 ). The study team may also consider biases arising from unmeasured confounding and plan quantitative bias analyses to explore how unmeasured confounding may impact estimates. Quantitative bias analysis can assess the directionality, magnitude, and uncertainty of errors arising from a variety of limitations (Brenner and Gefeller 1993 ; Lash et al. 2009 , 2014 ; Leahy et al. 2022 ).

3.6 Illustration using a previously published research study

In order to demonstrate how the guide can be used to plan a research study utilizing causal methods, we turn to a previously published study (Dondo et al. 2017 ) that assessed the causal relationship between the use of 𝛽-blockers and mortality after acute myocardial infarction in patients without heart failure or left ventricular systolic dysfunction. The investigators sought to answer a causal research question (Step 1), and so we proceed to Step 2. Use (or no use) of 𝛽-blockers was determined after discharge without taking into consideration discontinuation or future treatment changes (i.e., intention-to-treat). Considering treatment for whom (Step 3), both ATE and ATT were evaluated. Since survival was the primary outcome, an absolute difference in survival time was chosen as the effect measure (Step 4). While there was no explicit directed acyclic graph provided, the investigators specified a list of confounders.

Robust methodologies were established by consideration of possible sources of biases and addressing them using viable solutions (Steps 6 and 7). Table  1 offers a list of the identified potential biases and their corresponding solutions as implemented. For example, to minimize potential biases including prevalent-user bias and selection bias, the sample was restricted to patients with no previous use of 𝛽-blockers, no contraindication for 𝛽-blockers, and no prescription of loop diuretics. To improve balance across the comparator groups in terms of baseline confounders, i.e., those that could influence both exposure (𝛽-blocker use) and outcome (mortality), propensity score-based inverse probability of treatment weighting (IPTW) was employed. However, we noted that the baseline look-back period to assess measured covariates was not explicitly listed in the paper.

Quality control and sensitivity analysis (Step 8) is described extensively. The overlap of propensity score distributions between comparator groups was tested and confounder balance was assessed. Since observations in the tail-end of the propensity score distribution may violate the positivity assumption (Crump et al. 2009 ), a sensitivity analysis was conducted including only cases within 0.1 to 0.9 of the propensity score distribution. While not mentioned by the authors, the PS tails can be influenced by unmeasured confounders (Sturmer et al. 2021 ), and the findings were robust with and without trimming. An assessment of extreme IPTW weights, while not included, would further help increase confidence in the robustness of the analysis. An instrumental variable approach was employed to assess potential selection bias due to unmeasured confounding, using hospital rates of guideline-indicated prescribing as the instrument. Additionally, potential bias caused by missing data was attenuated through the use of multiple imputation, and separate models were built for complete cases only and imputed/complete cases.

4 Discussion

We have described a conceptual schema for designing observational real-world studies to estimate causal effects. The application of this schema to a previously published study illuminates the methodologic structure of the study, revealing how each structural element is related to a potential bias which it is meant to address. Real-world evidence is increasingly accepted by healthcare stakeholders, including the FDA (Concato and Corrigan-Curay 2022 ; Concato and ElZarrad 2022 ), and its use for comparative effectiveness and safety assessments requires appropriate causal study design; our guide is meant to facilitate this design process and complement existing, more specific, guidance.

Existing guidance for causal inference using observational data includes components that can be clearly mapped onto the schema that we have developed. For example, in 2009 Cox et al. described common sources of bias in observational data and recommended specific strategies to mitigate these biases, corresponding to steps 6–8 of our step-by-step guide (Cox et al. 2009 ). In 2013, the AHRQ emphasized development of the research question, corresponding to steps 1–4 of our guide, with additional chapters on study design, comparator selection, sensitivity analyses, and directed acyclic graphs which correspond to steps 7 and 5, respectively (Velentgas et al. 2013 ). Much of Girman et al.’s manuscript (Girman et al. 2014 ) corresponds with steps 1–4 of our guide, and the matter of equipoise and interpretability specifically correspond to steps 3 and 7–8. The current ENCePP guide on methodological standards in pharmacoepidemiology contains a section on formulating a meaningful research question, corresponding to step 1, and describes strategies to mitigate specific sources of bias, corresponding to steps 6–8 (European Medicines Agency 2023 ). Recent works by the FDA Sentinel Innovation Center (Desai et al. 2024 ) and the Joint Initiative for Causal Inference (Dang et al. 2023 ) provide more advanced exposition of many of the steps in our guide. The target trial framework contains guidance on developing seven components of the study protocol, including eligibility criteria, treatment strategies, assignment procedures, follow-up period, outcome, causal contrast of interest, and analysis plan (Hernán and Robins 2016 ). Our work places the target trial framework into a larger context illustrating its relationship with other important study planning considerations, including the creation of a directed acyclic graph and incorporation of prespecified sensitivity and quantitative bias analyses.

Ultimately, the feasibility of estimating causal effects relies on the capabilities of the available data. Real-world data sources are complex, and the investigator must carefully consider whether the data on hand are sufficient to answer the research question. For example, a study that relies solely on claims data for outcome ascertainment may suffer from outcome misclassification bias (Lanes and Beachler 2023 ). This bias can be addressed through medical record validation for a random subset of patients, followed by quantitative bias analysis (Lanes and Beachler 2023 ). If instead, the investigator wishes to apply a previously published, claims-based algorithm validated in a different database, they must carefully consider the transportability of that algorithm to their own study population. In this way, causal inference from real-world data requires the ability to think creatively and resourcefully about how various data sources and elements can be leveraged, with consideration for the strengths and limitations of each source. The heart of causal inference is in the pairing of humility and creativity: the humility to acknowledge what the data cannot do, and the creativity to address those limitations as best as one can at the time.

4.1 Limitations

As with any attempt to synthesize a broad array of information into a single, simplified schema, there are several limitations to our work. Space and useability constraints necessitated simplification of the complex source material and selections among many available methodologies, and information about the relative importance of each step is not currently included. Additionally, it is important to consider the context of our work. This step-by-step guide emphasizes analytic techniques (e.g., propensity scores) that are used most frequently within our own research environment and may not include less familiar study designs and analytic techniques. However, one strength of the guide is that additional designs and techniques or concepts can easily be incorporated into the existing schema. The benefit of a schema is that new information can be added and is more readily accessed due to its association with previously sorted information (Loveless 2022 ). It is also important to note that causal inference was approached as a broad overarching concept defined by the totality of the research, from start to finish, rather than focusing on a particular analytic technique, however we view this as a strength rather than a limitation.

Finally, the focus of this guide was on the methodologic aspects of study planning. As a result, we did not include steps for drafting or registering the study protocol in a public database or for communicating results. We strongly encourage researchers to register their study protocols and communicate their findings with transparency. A protocol template endorsed by ISPOR and ISPE for studies using real-world data to evaluate treatment effects is available (Wang et al. 2023a ). Additionally, the steps described above are intended to illustrate an order of thinking in the study planning process, and these steps are often iterative. The guide is not intended to reflect the order of study execution; specifically, quality control procedures and sensitivity analyses should also be formulated up-front at the protocol stage.

5 Conclusion

We outlined steps and described key conceptual issues of importance in designing real-world studies to answer causal questions, and created a visually appealing, user-friendly resource to help researchers clearly define and navigate these issues. We hope this guide serves to enhance the quality, and thus the impact, of real-world evidence.

Data availability

No datasets were generated or analysed during the current study.

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• Published: 17 June 2024

Mendelian randomization evidence for the causal effect of mental well-being on healthy aging

• Chao-Jie Ye   ORCID: orcid.org/0009-0009-7565-5048 1 , 2   na1 ,
• Dong Liu 1 , 2   na1 ,
• Ming-Ling Chen   ORCID: orcid.org/0000-0001-7992-1838 1 , 2   na1 ,
• Li-Jie Kong 1 , 2 ,
• Chun Dou 1 , 2 ,
• Yi-Ying Wang   ORCID: orcid.org/0000-0002-1252-7788 1 , 2 ,
• Min Xu   ORCID: orcid.org/0000-0003-3930-8718 1 , 2 ,
• Yu Xu 1 , 2 ,
• Mian Li   ORCID: orcid.org/0000-0001-6514-2729 1 , 2 ,
• Zhi-Yun Zhao   ORCID: orcid.org/0000-0001-5950-2732 1 , 2 ,
• Rui-Zhi Zheng 1 , 2 ,
• Jie Zheng 1 , 2 ,
• Jie-Li Lu   ORCID: orcid.org/0000-0003-1317-0896 1 , 2 ,
• Yu-Hong Chen 1 , 2 ,
• Guang Ning 1 , 2 ,
• Wei-Qing Wang   ORCID: orcid.org/0000-0001-6027-3084 1 , 2 ,
• Yu-Fang Bi   ORCID: orcid.org/0000-0002-4829-5915 1 , 2 &
• Tian-Ge Wang   ORCID: orcid.org/0000-0003-0723-489X 1 , 2  

Nature Human Behaviour ( 2024 ) Cite this article

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Mental well-being relates to multitudinous lifestyle behaviours and morbidities and underpins healthy aging. Thus far, causal evidence on whether and in what pattern mental well-being impacts healthy aging and the underlying mediating pathways is unknown. Applying genetic instruments of the well-being spectrum and its four dimensions including life satisfaction, positive affect, neuroticism and depressive symptoms ( n  = 80,852 to 2,370,390), we performed two-sample Mendelian randomization analyses to estimate the causal effect of mental well-being on the genetically independent phenotype of aging (aging-GIP), a robust and representative aging phenotype, and its components including resilience, self-rated health, healthspan, parental lifespan and longevity ( n  = 36,745 to 1,012,240). Analyses were adjusted for income, education and occupation. All the data were from the largest available genome-wide association studies in populations of European descent. Better mental well-being spectrum (each one Z -score higher) was causally associated with a higher aging-GIP ( β [95% confidence interval (CI)] in different models ranging from 1.00 [0.82–1.18] to 1.07 [0.91–1.24] standard deviations (s.d.)) independent of socioeconomic indicators. Similar association patterns were seen for resilience ( β [95% CI] ranging from 0.97 [0.82–1.12] to 1.04 [0.91–1.17] s.d.), self-rated health (0.61 [0.43–0.79] to 0.76 [0.59–0.93] points), healthspan (odds ratio [95% CI] ranging from 1.23 [1.02–1.48] to 1.35 [1.11–1.65]) and parental lifespan (1.77 [0.010–3.54] to 2.95 [1.13–4.76] years). Two-step Mendelian randomization mediation analyses identified 33 out of 106 candidates as mediators between the well-being spectrum and the aging-GIP: mainly lifestyles (for example, TV watching and smoking), behaviours (for example, medication use) and diseases (for example, heart failure, attention-deficit hyperactivity disorder, stroke, coronary atherosclerosis and ischaemic heart disease), each exhibiting a mediation proportion of >5%. These findings underscore the importance of mental well-being in promoting healthy aging and inform preventive targets for bridging aging disparities attributable to suboptimal mental health.

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Genotype × environment interactions in gene regulation and complex traits

Principal component-based clinical aging clocks identify signatures of healthy aging and targets for clinical intervention

Genome-wide association studies

Data availability.

All GWAS summary statistics analysed in this study are publicly available as shown in Table 1 and Supplementary Table 1 for download by qualified researchers. The GWAS data for mental well-being traits can be obtained from the GWAS catalogue 38 ( https://www.ebi.ac.uk/gwas/publications/30643256 ). The GWAS data for aging phenotypes can be retrieved or requested from the study authors at https://doi.org/10.7488/ds/2972 (the aging-GIP 14 ), https://doi.org/10.6084/m9.figshare.9204998.v3 (frailty index 42 ), http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST006001-GCST007000/GCST006620 (self-rated health 43 ), https://doi.org/10.5281/zenodo.1302861 (healthspan 44 ), https://doi.org/10.7488/ds/2463 (parental lifespan 45 ) and https://www.longevitygenomics.org/downloads (longevity 46 ). All data generated in this study are included in the Supplementary Information .

Code availability

All the MR analyses were conducted using R packages TwoSampleMR (version 0.5.7), MVMR (version 0.4), MRPRESSO (version 1.0) and MRlap (version 0.0.3.0) in R software (version 4.3.1). Custom code that supports the findings of this study is available at https://github.com/yechaojie/mental_aging .

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Acknowledgements

This work was supported by the grants from the National Natural Science Foundation of China (82370820, 82088102, 91857205, 823B2014 and 81930021), the ‘Shanghai Municipal Education Commission–Gaofeng Clinical Medicine Grant Support’ from Shanghai Jiao Tong University School of Medicine (20171901 Round 2), and the Innovative Research Team of High-level Local Universities in Shanghai. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The authors are grateful to the participants of all the GWASs used in this manuscript and the investigators who made these GWAS data publicly available.

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Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

Chao-Jie Ye, Dong Liu, Ming-Ling Chen, Li-Jie Kong, Chun Dou, Yi-Ying Wang, Min Xu, Yu Xu, Mian Li, Zhi-Yun Zhao, Rui-Zhi Zheng, Jie Zheng, Jie-Li Lu, Yu-Hong Chen, Guang Ning, Wei-Qing Wang, Yu-Fang Bi & Tian-Ge Wang

Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

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C.-J.Y. and T.-G.W. contributed to the conception and design of the study. C.-J.Y. performed statistical analyses and drafted the manuscript. T.-G.W. critically revised the manuscript. D.L., M.-L.C. and T.-G.W. checked the statistical analysis and proofread the manuscript. T.-G.W., G.N., W.-Q.W. and C.-J.Y. obtained funding. All authors contributed to the acquisition or interpretation of data, proofreading of the manuscript for important intellectual content and the final approval of the version to be published. T.-G.W. is the guarantor of this work and takes responsibility for the integrity of the data and the accuracy of the data analysis.

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Correspondence to Wei-Qing Wang , Yu-Fang Bi or Tian-Ge Wang .

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Ye, CJ., Liu, D., Chen, ML. et al. Mendelian randomization evidence for the causal effect of mental well-being on healthy aging. Nat Hum Behav (2024). https://doi.org/10.1038/s41562-024-01905-9

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Dark line represents the meta-analytical dose-response curve (constrained to be linear beyond upper knot at 75% of person-years). Shaded area displays 95% CI. Vertical dotted lines indicate knots at the 37.5th and 75th percentiles of person-years. I 2  = 73.7%; P  < .001. Interactive dose-response curves and exposure distributions are available online. 44

Dark lines represent the meta-analytical dose-response curve (constrained to be linear beyond upper knot at 75% of person-years). Shaded area displays 95% CIs. Vertical dotted lines indicate knots at the 37.5th and 75th percentiles of person-years. A, Major depression I 2  = 54.2%; P  = .01. B, Elevated depressive symptoms I 2  = 81.3%; P  < .001.

eMethods 1. Search strings

eMethods 2. Data imputation procedures

eMethods 3. Estimating the resting component of energy expenditure

eTable 1. Study inclusion and exclusion criteria

eTable 2. Study characteristics for analysis of heterogeneity

eTable 3. Relative risks using alternate assumptions

eTable 4. Potential impact fractions using alternate assumptions

eFigure 1. Study screening and selection flowchart

eFigure 2. Distribution of marginal MET hours per week

eFigure 3. Subgroup analysis

eFigure 4. Leave-one-out sensitivity analysis

eReferences

• Even Low Amounts of Physical Activity Reduce Depression Risk JAMA News From the JAMA Network June 7, 2022 Anita Slomski
• JAMA Network Articles of the Year 2022 JAMA Medical News & Perspectives December 27, 2022 This Medical News article is our annual roundup of the top-viewed articles from all JAMA Network journals. Melissa Suran, PhD, MSJ

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Pearce M , Garcia L , Abbas A, et al. Association Between Physical Activity and Risk of Depression : A Systematic Review and Meta-analysis . JAMA Psychiatry. 2022;79(6):550–559. doi:10.1001/jamapsychiatry.2022.0609

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Association Between Physical Activity and Risk of Depression : A Systematic Review and Meta-analysis

• 1 MRC Epidemiology Unit, University of Cambridge, Cambridge, England
• 2 Centre for Public Health, Institute of Clinical Sciences, Queen’s University Belfast, Belfast, Northern Ireland
• 3 Department of Sports Methods and Techniques, Federal University of Santa Maria, Santa Maria, Brazil
• 4 Cambridge University Hospitals NHS Foundation Trust, Cambridge, England
• 5 Physical Activity for Health Research Centre, Institute of Sport Physical Education and Health Science, University of Edinburgh, Edinburgh, Scotland
• 6 University of Cambridge School of Clinical Medicine, Addenbrooke’s Treatment Centre, Cambridge Biomedical Campus, Cambridge, England
• 7 Prevention Research Collaboration, Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
• 8 Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
• 9 Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Singapore
• 10 Sustainable Urban Programme, The Finnish Environment Institute, Helsinki, Finland
• 11 Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
• News From the JAMA Network Even Low Amounts of Physical Activity Reduce Depression Risk Anita Slomski JAMA
• Medical News & Perspectives JAMA Network Articles of the Year 2022 Melissa Suran, PhD, MSJ JAMA

Question   What is the dose-response association between physical activity and incident depression in adults?

Findings   This systematic review and meta-analysis of 15 prospective studies including more than 2 million person-years showed an inverse curvilinear association between physical activity and incident depression, with greater differences in risk at lower exposure levels. Adults meeting physical activity recommendations (equivalent to 2.5 h/wk of brisk walking) had lower risk of depression, compared with adults reporting no physical activity.

Meaning   In this study, relatively small doses of physical activity were associated with substantially lower risks of depression.

Importance   Depression is the leading cause of mental health–related disease burden and may be reduced by physical activity, but the dose-response relationship between activity and depression is uncertain.

Objective   To systematically review and meta-analyze the dose-response association between physical activity and incident depression from published prospective studies of adults.

Data Sources   PubMed, SCOPUS, Web of Science, PsycINFO, and the reference lists of systematic reviews retrieved by a systematic search up to December 11, 2020, with no language limits. The date of the search was November 12, 2020.

Study Selection   We included prospective cohort studies reporting physical activity at 3 or more exposure levels and risk estimates for depression with 3000 or more adults and 3 years or longer of follow-up.

Data Extraction and Synthesis   Data extraction was completed independently by 2 extractors and cross-checked for errors. A 2-stage random-effects dose-response meta-analysis was used to synthesize data. Study-specific associations were estimated using generalized least-squares regression and the pooled association was estimated by combining the study-specific coefficients using restricted maximum likelihood.

Main Outcomes and Measures   The outcome of interest was depression, including (1) presence of major depressive disorder indicated by self-report of physician diagnosis, registry data, or diagnostic interviews and (2) elevated depressive symptoms established using validated cutoffs for a depressive screening instrument.

Results   Fifteen studies comprising 191 130 participants and 2 110 588 person-years were included. An inverse curvilinear dose-response association between physical activity and depression was observed, with steeper association gradients at lower activity volumes; heterogeneity was large and significant ( I 2  = 74%; P  < .001). Relative to adults not reporting any activity, those accumulating half the recommended volume of physical activity (4.4 marginal metabolic equivalent task hours per week [mMET-h/wk]) had 18% (95% CI, 13%-23%) lower risk of depression. Adults accumulating the recommended volume of 8.8 mMET hours per week had 25% (95% CI, 18%-32%) lower risk with diminishing potential benefits and higher uncertainty observed beyond that exposure level. There were diminishing additional potential benefits and greater uncertainty at higher volumes of physical activity. Based on an estimate of exposure prevalences among included cohorts, if less active adults had achieved the current physical activity recommendations, 11.5% (95% CI, 7.7%-15.4%) of depression cases could have been prevented.

Conclusions and Relevance   This systematic review and meta-analysis of associations between physical activity and depression suggests significant mental health benefits from being physically active, even at levels below the public health recommendations. Health practitioners should therefore encourage any increase in physical activity to improve mental health.

Depression is the leading cause of mental health–related disease burden and a major cause of disability worldwide, affecting approximately 280 million people and accounting for more than 47 million disability-adjusted life-years in 2019. 1 Depression is also associated with premature mortality from other illnesses 2 and suicide. 3

Prevention of depression requires effective interventions, including modification of established risk factors. 4 Narrative reviews have concluded that physical activity can prevent future depression. 5 , 6 One meta-analysis of prospective studies reported that compared with people with low levels of physical activity, those with higher levels had 17% (95% CI, 12%-21%) lower odds of developing depression, 7 while another meta-analysis reported 21% (95% CI, 18%-25%) lower odds when synthesizing 106 associations from 65 studies using diverse exposure definitions. 8 To our knowledge, no study has yet synthesized the evidence to describe the strength or shape of the association by conducting a dose-response meta-analysis using harmonized exposure estimates.

Estimating the dose-response relationship between physical activity and any health outcome using meta-analysis is challenging because of the diversity of assessment and inconsistent reporting. Most often, the harmonization of different estimates of physical activity exposure is achieved using binary categorization of low vs high activity, but this approach results in loss of information and does not tell us about the variation in risk across a range of physical activity doses. In contrast, previous work has shown that by using more detailed exposure harmonization, it is possible to investigate the shape of the association between physical activity and type 2 diabetes using dose-response meta-analytical techniques. 9 Examining the association between the dose of physical activity and depression in this way allows approximation of the reduction in risk associated with different levels of activity. By combining estimates of reduction in risk with population prevalence estimates of activity, it is possible to quantify the population burden of depression related to insufficient physical activity and the potential public health impact of activity-related interventions.

The aim of this systematic review and meta-analysis was to investigate the dose-response association between physical activity and depression. We also assessed the potential population changes in depression that may be preventable by higher physical activity levels.

The study protocol is available at PROSPERO ( CRD42018095507 ) and followed Meta-analysis of Observational Studies in Epidemiology ( MOOSE ) reporting guidelines.

We included prospective cohort studies of adults (≥18 years of age) that reported any dimension of physical activity at 3 or more exposure levels and reported risk estimates for depression. Studies were excluded if the sample size was fewer than 3000 participants (to limit small-study effects indicative of publication bias) or if the follow-up period was less than 3 years (to minimize reverse causality bias from undiagnosed depression at baseline). Full details are provided in eTable 1 in the Supplement . Studies were eligible if the exposure included leisure-time physical activity, either alone or combined with other activity domains. The outcome of interest was depression, including (1) presence of major depressive disorder indicated by self-report of physician diagnosis, registry data, or diagnostic interviews using DSM criteria or International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, codes F32 through F33; (2) elevated depressive symptoms established using validated cutoffs for a depressive screening instrument, eg, Center for Epidemiologic Studies Depression scale.

We searched PubMed, SCOPUS, Web of Science, PsycINFO, and the reference lists of systematic reviews and articles retrieved from the search or known to the authors. Specific search strings are available in eMethods 1 in the Supplement . We considered peer-reviewed articles published in academic journals until November 12, 2020. No language limits were set. Titles and abstracts and subsequently full-text articles were screened by 2 independent reviewers for eligibility, with disagreements resolved by a third reviewer (eFigure 1 in the Supplement ).

Data extraction was carried out using a standardized extraction form and conducted independently by 2 researchers, with disagreements resolved by a third researcher. For each exposure category, we extracted information to estimate physical activity volume if not reported directly, number of depression cases, number of participants and person-years of follow-up, and the effect estimate with 95% CIs. Effect estimates from the most adjusted model were used. Results reported separately for men and women, or for multiple cohorts within a study, were extracted as separate associations.

When the data required for exposure harmonization or meta-analysis were not reported in the original articles, nor obtainable through other publications from the same cohort, we contacted authors before imputing critical information if necessary (eMethods 2 in the Supplement ).

We harmonized reported exposure levels to a common metric of physical activity volume in marginal metabolic equivalent task hours per week (mMET-h/wk), reflecting the energy expended above the resting metabolic rate (1 MET). Combinations of harmonization techniques were used depending on what was reported or obtained from authors and availability of validation work (details of the original exposure data, harmonization methods, and harmonization flowchart are available in an Open Science Framework 10 repository).

Studies that described physical activity exposure as frequency and duration were converted to a measure of weekly duration. If session duration was not provided, a session duration of 0.75 hours was assumed. Weekly durations were then converted to activity volume in mMET hours per week by multiplying by intensity values of 1.5 mMET for light, 3.5 mMET for moderate and moderate to vigorous, and 7.0 mMET for vigorous physical activity. 11 A score of 3.5 mMET corresponds to the midpoint of the range for moderate-intensity activity (≥2 to <5 mMETs). Vigorous was scored at 7 mMET because activity of this intensity contributes twice as much toward meeting physical activity guidelines compared with moderate activity (the World Health Organization advises 150-300 minutes of moderate intensity activity or 75-150 minutes of vigorous intensity activity per week 12 ). Light activities are those in the range of 0.5 to 2 mMETs; however, these were scored slightly higher than the midpoint at 1.5 mMET to reflect the intensity of behaviors commonly referred to as light activity in questionnaires (eg, light housework, light gardening).

One study reported energy expenditure without adjustment for body weight (kcal/wk). Weight was calculated using body mass index and height from national survey data. 13 The exposure in kcal per week was then divided by derived weight and further converted to MET hours using 1 kcal/kg = 1 MET hour.

For studies reporting physical activity volume and corresponding duration data, 1 MET hour per week was subtracted for each hour of activity undertaken; this is equivalent to the original studies having multiplied activity duration with net (mMET) rather than gross (MET) intensity values. If duration for each volume level was not reported or obtainable from authors, we used a prediction equation derived from studies where both volume and duration were available (eMethods 3 in the Supplement ).

Nonoccupational physical activity was used as the exposure in all but 1 study 14 that used total physical activity, including leisure-time, domestic, occupational, and transport domains. Given no domain-specific activity results were available for this study, we assumed occupational activity to be 40 hours per week at 1.25 METs (or 0.25 mMETs, ie, equivalent to sedentary office work) and estimated nonoccupational physical activity by subtracting this assumed occupational volume.

We conducted dose-response meta-analyses for outcomes with at least 4 independent studies available. For studies reporting only sex-stratified results, strata-specific risks were combined using fixed-effect meta-analysis. Sex-stratified meta-analyses were not conducted because only 2 sets of sex-stratified results were available.

A 2-stage random-effects meta-analysis was used to model the dose-response association between physical activity and depression. First, study-specific associations were estimated using generalized least-squares regression. 15 , 16 Second, we estimated the pooled association by combining the study-specific coefficients using restricted maximum likelihood. 17 We assumed the dose-response associations were nonlinear and modeled them by fitting restricted cubic splines with 3 knots at the 0, 37.5th, and 75th percentiles of person-years. If the statistical model was unable to converge for these knots, we increased the percentile for the upper knot until it did.

To provide a population perspective of the relative importance of the estimated dose-response associations, potential impact fractions (PIFs) were calculated based on the exposure prevalence in the populations of included cohorts. 18 PIFs were calculated for 3 exposure levels based on the World Health Organization recommendations for adults 12 : the minimum recommended level of 8.8 mMET hours per week (volume equivalent to approximately 2.5 h/wk of physical activity at moderate intensity of 3.5 marginal METs), the level recommended for additional health benefits of 17.5 mMET hours per week, as well as 4.4 mMET hours per week (half the minimum recommended level).

We conducted 2 sensitivity analyses. First, we assigned 2.5 and 6 mMET to moderate and vigorous physical activity, respectively, when intensity had to be assumed for physical activity exposure harmonization; for studies requiring assumptions about duration of sessions, we assigned a 0.5-hour duration (rather than 0.75-hour duration). Second, we tested the use of knots at the 0, 42.5th, and 85th percentiles in the cubic spline functions.

We conducted subgroup analyses by depression diagnosis method using 2 outcome subtypes: (1) major depression from registry data, self-report of physician diagnosis, or diagnostic interview; (2) use of a screening tool for elevated depressive symptoms with validated cutoffs.

Cochran Q test and I 2 statistic were used to assess heterogeneity of effect measures between studies. We also conducted random-effect meta-regressions (at the physical activity level of 8.8 mMET-h/wk) using restricted maximum likelihood to identify how much of the variance in effect measures was due to proportion of men and women (women only, men only, mixed), outcome ascertainment (registry data, self-report of physician diagnosis, diagnostic interview, Center for Epidemiologic Studies Depression scale, other depressive symptoms scale), duration of follow-up (above or below median of 8.5 years), exclusion of prevalent cases at baseline (yes, no), handling other morbidities at baseline (exclusion of participants, statistical adjustment), and our exposure harmonization approaches (measurement unit conversion, measurement unit conversion plus occupational physical activity assumption, duration/intensity/frequency assumptions). See eTable 2 in the Supplement for more details. We conducted a leave-one-out sensitivity analysis to investigate the influence of each study on the overall effect-size (at the physical activity level of 8.8 mMET-h/wk).

Analyses were performed using R, version 4.0.5, and the dosresmeta package, 19 version 2.0.1. An interactive interface to visualize dose-response curves was developed using the Shiny package, version 1.0.5. Codes for all analyses and the interactive interface are available in a repository on GitHub 20 (see README file).

The systematic literature search yielded 19 175 results following removal of duplicates. After excluding 18 827 records based on title and abstract screening, 348 full-text articles were reviewed. Full-text screening identified 15 eligible publications reporting 15 associations including 191 130 participants contributing 28 806 incident depression events and 2 110 588 person-years. Approximately 64% of participants in the studies were women. All but 1 of the included studies originated in high-income countries: 6 from the United States, 21 - 26 6 from Europe, 27 - 32 1 from Australia, 33 1 from Japan, 34 and 1 study that included data from India, Ghana, Mexico, and Russia. 14 Study characteristics are described in Table 1 . 14 , 21 - 43

The majority (78%) of participants reported exposure levels below 17.5 mMET hours per week, with almost all (95%) data below 35 mMET hours per week; participants in studies of elevated depressive symptoms tended to be less active than those in studies of major depression (eFigure 2 in the Supplement ). Figure 1 shows an inverse, curvilinear dose-response association between physical activity and depression, with greater differences in risk in the lower-dose region. Relative to adults not reporting any activity, those accumulating half the recommended volume of physical activity (4.4 mMET-h/wk) had 18% (95% CI, 13%-23%) lower risk of depression. Adults accumulating the recommended volume of 8.8 mMET hours per week had 25% (95% CI, 18%-32%) lower risk with diminishing potential benefits and higher uncertainty observed beyond that exposure level ( Table 2 ). Interactive dose-response curves and exposure distributions are available online. 44 The dose-response curves for major depression and elevated depressive symptoms showed similar curvilinear relationships ( Table 2 and Figure 2 ).

Based on PIF analyses of prevalences of physical activity and depression outcomes in the included cohorts, 11.5% (95% CI, 7.7%-15.4%) of incident depression could have been prevented if all adults had achieved at least 8.8 mMET hours per week of physical activity ( Table 2 ). PIFs were approximately twice as high for elevated depressive symptoms than for major depression at both 8.8 and 17.5 mMET hours per week and approximately 3 times higher at 4.4 mMET hours per week ( Table 2 ).

Using alternative assumptions of 0.5 hours for session duration and 1 MET lower-intensity values for moderate and vigorous physical activity (only when those assumptions were necessary) or alternative placement of knots for the splines did not materially alter dose-response associations (eTable 3 in the Supplement ) or PIFs (eTable 4 in the Supplement ).

Differences between proportion of women, exposure harmonization method, outcome ascertainment, duration of follow-up, exclusion of prevalent cases at baseline, or managing other morbidities at baseline did not significantly explain variance in the effect measures of associations between physical activity and depression (eFigure 3 in the Supplement ). The leave-one-out sensitivity analysis did not identify any outliers (eFigure 4 in the Supplement ).

This study reports the first dose-response meta-analysis of associations between physical activity and incident depression to our knowledge. Our results show an inverse curvilinear association with the greatest differences in risk observed between low doses of physical activity, suggesting most benefits are realized when moving from no activity to at least some. Accumulating an activity volume equivalent to 2.5 hours of brisk walking per week was associated with 25% lower risk of depression, and at half that dose, risk was 18% lower compared with no activity. Only minor additional benefits were observed at higher activity levels.

Previous meta-analyses have shown lower risks of depression among adults reporting high vs low physical activity 7 and suggested a dose-response association using meta-regression without quantifying the association. 8 Our study directly models how risk of depression varies across the physical activity exposure range using a continuous scale rather than cruder categories. We also found that even small volumes of activity were beneficial but go further by quantifying differences in risk for these doses. Our findings therefore have important new implications for health practitioners making lifestyle recommendations, especially to inactive individuals who may perceive the current recommended target as unrealistic.

The associations we observed are likely explained by more than 1 mechanism. Proposed pathways include acute neuroendocrine and inflammatory responses to activity such as activation of the endocannabinoid system (“runner’s high”) 45 and longer-term adaptations, including changes in the brain’s neural architecture. 46 Psychosocial and behavioral explanations have also been suggested, including improved physical self-perceptions and body image, more social interactions, and the personal development of coping strategies. 46 The social aspect of activity participation may operate even at relatively low doses, consistent with the dose-response curve we observed. The role of the environment as a potential moderator of the association between physical activity and depression should also be considered. For example, the use of green space is associated with lower risk of depression, 47 with mediation analysis suggesting only part of the association is explained by physical activity. 48 Conversely, noise pollution 49 and neighborhood deprivation 50 might diminish the mental health benefits of activity. Such contextual factors may have contributed to the high level of heterogeneity we observed between studies. The above mechanisms may not operate to the same degree across different types, frequencies, intensities, and contexts of activity, 51 and measurement of these activity subdimensions may also differ between studies, potentially resulting in higher heterogeneity. Future work should therefore explore the shape of the dose-response relationship for these aspects of activity in addition to total volume.

In subgroup analyses using results from different cohorts for each outcome, we observed that the dose response with physical activity was similar for major depression and elevated depressive symptoms, as has been reported using binary exposure expressions. 7 However, PIFs for elevated depressive symptoms were higher; this is because participants in those studies were generally less active than participants in the studies examining major depression. Examining depressive symptoms on a continuous scale instead of using dichotomized outcome variables may provide further insight regarding the benefits of physical activity to alleviate depressive symptoms, but no study that we reviewed reported results in this way.

To conduct nonlinear dose-response analyses, we only included studies with at least 3 levels of physical activity exposure, a requirement that limited the number of eligible results compared with previous meta-analyses. 7 , 8 Physical activity exposures in primary studies were classified using a variety of methods and expressed in different formats, and this could have resulted in excluding even more studies. A key strength of the present meta-analytical work is our extensive exposure harmonization, maximizing the inclusion of the existing evidence. We did observe high heterogeneity between study estimates, but differences in method of exposure harmonization did not explain variation in effect sizes. Duration of follow-up, proportions of men and women, original analytical choices relating to the handling of other morbidities or prevalent depression at baseline, and method of depression diagnosis were not statistically significant predictors of effect size.

It is still possible that the associations observed in the present analyses could overestimate the role of physical activity because of reverse causality. That is, depression at baseline results in lower physical activity in participants who then go on to record depression during follow-up. For depression diagnosed at baseline, this bias is often mitigated by excluding these prevalent cases from analyses. Two of the studies included in our meta-analysis did not exclude participants with depression at baseline or adjust for these statistically, 24 , 33 and our subgroup analysis indicated (although not statistically significantly so) that effect sizes of this subgroup were stronger than those from studies that excluded prevalent cases. Reverse causality bias may also result from depression that is undiagnosed at baseline, particularly given the often recurring nature of the condition. 52 To limit risk of bias from undiagnosed depression, we only included studies with at least 3 years of follow-up. This does not rule out the possibility that physical activity may be causally associated with depression on a shorter time scale. Two studies included in our full-text screening were excluded solely based on follow-up time. A study of 108 000 South Korean adults reported an inverse association for physical activity in the exposure range approximating 7 to 40 mMET hours per week, but no association at very high activity levels. 53 A study of 4600 Irish adults reported lower (albeit not statistically significant) odds of depression in those meeting physical activity guidelines compared with those who did not. 54

Longer follow-up time limits the risk of reverse causality affecting the results but can also introduce regression dilution bias owing to exposure measurement error caused by true variation in physical activity behavior during follow-up. 55 For example, in a recent study, mortality associations estimated using 28 years’ follow-up with a single measure of physical activity at baseline were 2- to 3-fold weaker than those estimated using repeated measures. 56 We did not include results using more than 1 measure of physical activity because none were identified that met our inclusion criteria. However, we did observe weaker associations in studies with longer follow-up times, although again, this meta-regression result was not statistically significant. Given the recurrent nature of depression and engagement in physical activity as a treatment for this condition, 57 use of repeated measures may be particularly important and would strengthen the evidence base for evaluating the dose-response relationship. Further work is required to understand the effects of different strategies for managing reverse causality and exposure measurement error in studies of depression.

A limitation of these analyses is that included studies only used self-reported measures of physical activity, which are subject to recall and social-desirability biases. Our analyses also included relatively limited data at higher physical activity doses. Studies using device-based measures of activity capturing a wider range of exposure and with longer follow-up of incident depression are therefore warranted. There were insufficient studies with stratified results to examine sex, age, or geographical subgroups, and notably, lower- and middle-income countries were underrepresented in included studies.

This meta-analysis found an association between physical activity and incident depression. This suggests substantial mental health benefits can be achieved at physical activity levels even below the public health recommendations, with additional benefit for meeting the minimum recommended target but limited extra benefit beyond that. Assuming causality, 1 in 9 cases of depression might have been prevented if everybody in the population was active at the level of current health recommendations.

Accepted for Publication: February 10, 2022.

Published Online: April 13, 2022. doi:10.1001/jamapsychiatry.2022.0609

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2022 Pearce M et al. JAMA Psychiatry .

Corresponding Author: James Woodcock, PhD, MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Level 3 Institute of Metabolic Science, Addenbrooke’s Treatment Centre, Cambridge Biomedical Campus, Cambridge CB2 0QQ, England ( [email protected] ).

Author Contributions : Drs Pearce and Woodcock had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs Pearce, Garcia, and Abbas are joint first authors. Drs Brage and Woodcock are joint senior authors.

Concept and design: Pearce, Garcia, Abbas, Schuch, Kelly, Mok, Brage, Woodcock.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Pearce, Garcia, Schuch, Mok, Brage, Woodcock.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Pearce, Garcia, Abbas, Brage.

Obtained funding: Brage, Woodcock.

Administrative, technical, or material support: Abbas, Strain, Schuch, Mok, Smith.

Supervision: Abbas, Schuch, Brage, Woodcock.

Conflict of Interest Disclosures: Dr Garcia reported grants and nonfinancial support from the Medical Research Council and nonfinancial support from the British Heart Foundation, Cancer Research UK, Economic and Social Research Council, National Institute for Health Research, and Wellcome Trust during the conduct of the study. Dr Brage reported grants from the Medical Research Council (core program grant) during the conduct of the study. Dr Woodcock reported grants from the Medical Research Council, European Research Council, Wellcome Trust, British Heart Foundation, Economic and Social Research Council, Cancer Research UK, and Department of Health during the conduct of the study. No other disclosures were reported.

Funding/Support: Drs Pearce, Strain, and Brage were supported by the UK Medical Research Council (MC_UU_12015/3, MC_UU_00006/4). Drs Pearce and Brage were supported by the National Institute for Health Research (NIHR) Biomedical Research Centre Cambridge (IS-BRC-1215-20014). Drs Pearce, Abbas, and Woodcock were supported by the European Research Council (ERC) under the Horizon 2020 Research and Innovation Programme (grant agreement 817754). Drs Garcia, Abbas, and Woodcock were supported by METAHIT, an MRC Methodology Panel project (MR/P02663X/1), and the Centre for Diet and Activity Research, a UKCRC Public Health Research Centre of Excellence funded by the British Heart Foundation, Cancer Research UK, Economic and Social Research Council, Medical Research Council, NIHR, and the Wellcome Trust (MR/K023187/1). Dr Schuch was supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior in Brasil (finance code 001). Dr Golubic was supported by a Gates Cambridge Scholarship. Dr Mok was supported by the National Science Scholarship from the Singapore Agency for Science, Technology and Research.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: This material reflects only the author’s views, and the European Commission is not liable for any use that may be made of the information contained therein.

Additional Contributions: We thank the Information Technology team of the MRC Epidemiology Unit (University of Cambridge) for setting up the server to host the online results. They received no compensation beyond salaries for this contribution.

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• Open access
• Published: 24 June 2024

Effects of memory and attention on the association between video game addiction and cognitive/learning skills in children: mediational analysis

• Amani Ali Kappi 1 ,
• Rania Rabie El-Etreby 2 ,
• Ghada Gamal Badawy 3 ,
• Gawhara Ebrahem 3 &
• Warda El Shahat Hamed 2  

BMC Psychology volume  12 , Article number:  364 ( 2024 ) Cite this article

Metrics details

Video games have become a prevalent source of entertainment, especially among children. Furthermore, the amount of time spent playing video games has grown dramatically. The purpose of this research was to examine the mediation effects of attention and child memory on the relationship between video games addiction and cognitive and learning abilities in Egyptian children.

A cross-sectional research design was used in the current study in two schools affiliated with Dakahlia District, Egypt. The study included 169 children aged 9 to 13 who met the inclusion criteria, and their mothers provided the questionnaire responses. The data collection methods were performed over approximately four months from February to May. Data were collected using different tools: Socio-demographic Interview, Game Addiction Scale for Children (GASC), Children’s Memory Questionnaire (CMQ), Clinical Attention Problems Scale, Learning, Executive, and Attention Functioning (LEAF) Scale.

There was a significant indirect effect of video game addiction on cognitive and learning skills through attention, but not child memory. Video game addiction has a significant impact on children’s attention and memory. Both attention and memory have a significant impact on a child’s cognitive and learning skills.

Conclusions

These results revealed the significant effect of video game addiction on cognitive and learning abilities in the presence of mediators. It also suggested that attention-focused therapies might play an important role in minimizing the harmful effects of video game addiction on cognitive and learning abilities.

Peer Review reports

Introduction

The use of video games has increased significantly in recent years. Historically, such games are used more often by children. Despite the positive impacts of video games on socialization and enjoyment, empirical and clinical research has consistently demonstrated that many children can become addicted due to excessive use. Among Arab children and adolescents, the prevalence of video game addiction is 62% of 393 adolescents in Saudi Arabia, 5% in Jordan, 6% in Syria, and 7.8% in Kuwait [ 1 , 2 ]. The varying incidence rates can be attributable to variations in the research population, cultural determinants, and evaluation or diagnostic standards.

In addition, video games, the internet, and other new technologies have become children’s top leisure pursuits. Today, they comprise a virtual environment in which thousands of gamers simultaneously participate worldwide; rather than being a personal or lonely leisure activity, they are often a group activity that establishes new social networks [ 3 ]. Although playing video games in moderation can have many positive effects, their exploitation may lead to addictions and societal issues [ 4 ]. The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), identifies repetitive and persistent behavior related to online video games as the core element of addiction. This behavior should persist for at least 12 months and result in significant impairment. Additionally, addiction should be accompanied by psychological and social symptoms, as well as tolerance and withdrawal symptoms [ 5 ].

Different studies have examined the impact of video games on children’s cognitive abilities and school performance [ 6 , 7 ]. The recent literature has shown how video games affect the brain and alter its functioning while being played. It demonstrates how specific cortical and subcortical structures are involved [ 8 , 9 , 10 ]. Research indicates that excessive play of the same typees of games might negatively impact school-age children’s cognitive and academic skills as well as their capacity to maintain and enhance memories [ 7 ]. Possible consequences of video game addiction may include memory and attention-related difficulties [ 4 , 6 , 11 ]. For instance, children’s memory scores negatively correlated with greater levels of video game addiction in Lebanon [ 6 ]. Furthermore, studies show that action-game players are more likely to succeed at short-term concentration tests while they perform below average in long-term, less exciting activities. At the point of game addiction, difficulties with focus are likely to become much more apparent [ 12 ]. Studies show a substantial association between gaming addiction and inattention, even after controlling other variables such as personality factors, anxiety and depression symptoms, and attention deficit hyperactivity disorder [ 13 , 14 ].

Prior studies have illustrated the association between video game addiction and psychiatric disorders, social phobia, mental well-being, and risky health behaviors [ 15 , 16 , 17 ]. Another study shows an association between video game addiction and memory, attention, cognitive, and learning abilities among Lebanese children [ 18 ]. However, all of these studies explain the association without controlling for any history of mental or behavioral disorders such as ADHD, anxiety, or depression. However, to the best of our knowledge, a few studies have specifically investigated the effect of attention and child memory on the relationship between video game addiction and cognitive and learning abilities in Egyptian children. Therefore, this study aimed to explore the mediation effect of attention and child memory on the association between addiction to video game and cognitive and learning abilities among Egyptian children. Our hypotheses were: (1) child attention mediates the relationship between video game addiction and cognitive and learning abilities among Egyptian children; and (2) child memory mediates the relationship between video game addiction and cognitive and learning abilities among Egyptian children.

Literature review

Video games have transformed into complex experiences that embody principles recognized by psychologists, neuroscientists, and educators as crucial for behavior, learning, and cognitive functions. While video games offer social and entertainment benefits, extensive research indicates that their excessive use can lead to adverse psychological consequences and even addiction in a minority of players. Symptoms like impaired control over gaming and prioritizing games over daily responsibilities may signify gaming addiction [ 19 ].

The Diagnostic and Statistical Manual of Mental Disorders (DSM-5) acknowledged video game addiction as an internet gaming disorder in its fifth edition, highlighting the need for further research [ 20 ]. Similarly, the 11th edition of the International Classification of Diseases (ICD-11) classified gaming disorder as a recurrent pattern of gaming behavior that encompasses both online and offline gaming [ 21 ]. Scientific evidence indicates that addictions can develop due to a combination of genetic susceptibility and repeated exposure to specific stimuli [ 22 ].

Growing public concerns have emerged regarding the potential negative impacts of video games, notably on children’s memory [ 23 ]. Individuals with various behavioral disorders and those with addictive tendencies often find their memory, crucial for comprehension and cognitive abilities like memory updating and working memory, compromised [ 24 ]. Although some research delves into video games’ effects on cognitive functions and academic achievement in children [ 25 , 26 ], the impact on memory remains a contentious topic.

Despite being a leisure activity, video gaming can pose issues for certain children, impacting their ability to focus. Meta-analysis and systematic reviews by Ho et al. and Carli et al. indicated a link between inattention and addiction to the internet and gaming [ 27 ]. Additionally, numerous studies corroborated this connection, demonstrating a robust correlation between the severity of inattention in ADHD and addiction to the internet or gaming. This correlation persisted even after controlling for factors such as depression and anxiety symptoms, as well as personality traits [ 27 ].

Study design and sample

This study has a cross-sectional descriptive design. It was conducted in two convienient selected preparatory schools, Emam Mohamed Abdo Preparatory and Omar Ibn Elkhatab Preparatory School. The two schools are affiliated with xxx. The participants were selected at random from the list of school principals. The research was open to all students between the ages of 9 and 13 with no history of physical, mental, or cognitive disorders. Each student’s parents provided the questionnaire responses. Using the G-power software 3.1.9.2, the study’s sample size was determined. Based on an average effect size of f = 0.15, a 2-sides test at alpha = 0.05, a statistical power (1-β) of 0.95, and eight predictors (age, gender, educational level of the child and mother, video game addiction, memory, attention, and learning abilities), power analysis was performed. A minimum of 166 participants were required based on these criteria.

Ethical consideration

The study approved by the Research Ethics Committee (REC) of Mansoura University’s Faculty of Nursing (IRB P0506/9/8/2023). The study’s purpose, methodology, duration, and benefits were also explained to the directors of the two selected institutions. Mothers’ consents obtained after explaining the study’s objective and the data kept confidential. The participants were informed that they had their right to withdraw from the study at any time.

Data Collection

The following tools were utilized in the study:

Socio-demographic questionnaire

Child and mother’s information was collected, such as age, sex, number of children, and level of education.

• Video game addiction

We used the Game Addiction Scale for Children (GASC) to measure children’s video game addiction. The GASC developed by Yılmaz, Griffiths [ 28 ] according to DSM criteria to evaluate gaming addiction. It includes 21 self-reported items rated on five-point Likert scale (from 1 = never to 5 = very frequently), where higher score shows more hazardous online gaming usage. An individual’s total score can range from a minimum of 21 to a maximum of 105; a score above 90 may be a sign of a video game addiction. It is also emphasized that this is not a diagnostic tool, however, but merely an indicator that a child may have a gaming addiction. Such a diagnosis could only be made by a comprehensive clinical evaluation. Seven criteria for video game addiction are determined by the scale: salience, tolerance, mood modification, withdrawal, relapse, conflict, and issues. The scale shows an acceptable internal consistency reliability ( r  = 0.89, p  < 0.001) [ 19 ].

Children’s memory

We used the Children’s Memory questionnaire (CMQ) to assess children’s memory rated by their parents. The CMQ developed by Drysdale, Shores [ 29 ]. It included 34 items that rated on a five-point Likert scale ranging from 1 = never or almost never, to 5 = more than once a day. Higher scores indicate a more significant reduction in the cognitive domain. The scale is divided into three subscales: working memory and attention, visual memory, and episodic memory. The Cronbach alpha value for the episodic memory subscale was 0.88, the visual memory is 0.77, and the working memory is 0.84 [ 29 ].

Attention of children

The Clinical Attention Problems Scale was used to measure children’s attention level in the morning and afternoon. This scale was developed by Edelbrock and Rancurello [ 30 ] and includes 12 items. The possible responses are 0 = not true, 1 = somewhat or sometimes true, and 2 = very often or often true. The higher the scores, the more attention there is. The Cronbach alpha values for the clinical attention problem in the morning is 0.84 and for the afternoon is 0.83.

Cognitive and learning skills

We used the Learning, Executive, and Attention Functioning (LEAF) scale to measure children’s cognitive and learning skills. The LEAF scale is a self-reported 55 items scale developed by Castellanos, Kronenberger [ 31 ]. The scale assesses core cognitive abilities and related academic and learning abilities. The LEAF assesses cognitive skills such as attention, processing speed, working memory, sustained sequential processing to accomplish goals (such as planning and carrying out goal-directed tasks), and new problem-solving. Moreover, the LEAF approach takes into account academic functioning, declarative/factual memory, and understanding and concept formulation.

The LEAF includes 55 items, with 11 academic subscales that rate a person’s reading, writing, and mathematics proficiency. The LEAF is divided into subscales that measure comprehension and conceptual learning, factual memory, attention, processing speed, visual-spatial organization, sustained sequential processing, working memory, new problem-solving, mathematics, basic reading, and written expression skills. Each subscale has the same number of items. The responses were rated on a three-point scale ranging from 0 to 3. Higher scores indicate more significant issues with cognition. The five component items are added to provide the subscale score for each of the 11 subject areas. Three criterion-referenced ranges are established for the interpretation of LEAF subscale raw scores. Out of nine, a score of five to nine is classified as the “borderline problem range,” a score of less than five as the “no problem range,” and a score of nine or above as the “problem range.” The Cronbach alpha value for the LEAF scale is 0.96.

Validity and reliability

Study tools were translated into Arabic by the researchers. Five pediatric nursing and psychiatric and mental health nursing experts tested them for content validity. At first, the scales were translated into Arabic using a forward and backward translation method. The translated questionnaires were then adapted to fit Arabic cultural norms. Two highly proficient native Arabic speakers who are accomplished academics in the fields of psychiatry and mental health nursing, and hold the academic status of Full Professor translated the questionnaire from English to Arabic. An English-language expert who is fluent in Arabic back translated the Arabic version. Native Arabic speakers who were not involved in the translation process verified the final translation. The forward-to-back translation process was repeated until the comparative findings matched exactly. The questionnaires were then given to three Arabic psychiatric nursing professionals, who provided their opinions on its importance, relevance, and simplicity. The tools’ reliability was tested using Cronbach’s alpha test (tool I α = 0.86, tool II α = 0.81, tool III α = 0.95, and tool IV α = 0.95, respectively). Additionally, a confirmatory factor analysis were carried out to validate the content of the four scales after translation. The data collection methods were performed over approximately four months from February to May. Also, a pilot study was conducted to assess the study tools’ feasibility and determine the time required to complete the tools. 10% of the initial participants were randomly selected from the same schools. Minimal modifications were then made to the tools. Mothers of students who participated in the pilot study were excluded from the primary study. The data was collected for four months (February to May). An online Google form was created to collect data. The link was then shared with selected student parents through WhatsApp groups. The link outlined the study’s purpose and methods, and participants signed a consent form.

Data collection procedure

We obtained permission to translate the study scales into Arabic. We collected data from February to May using an online Google Form for four months. The Google Form included full details regarding the study’s aims and processes to ensure transparency and establish participants’ trust. An extensive description of the response process additionally supports the Attention Problems Scale. For instance, mothers are required to respond to the items and their relevance to their children in the morning and afternoon. We distributed the survey link to the selected students’ mothers through WhatsApp groups as it was convenient and widespread among the target demographic. Before proceeding to the survey questions, participants were required to read and sign this consent form to ensure that participants received information about the study and voluntarily consented.

Statistical analysis

We employed the Statistical Package for Social Science version 26 [ 23 ] to analyze the data. We analyzed the demographic data using descriptive statistics such as means, standard deviations, frequency, and percentages. In order to evaluate the mediator effects of memory and attention on the relationship between cognitive, academic, and learning skills and video gaming addiction, we ran the multiple regression PROCESS macro with 5,000 bootstraps in SPSS version 3.4 [ 24 ]. We also included confounding variables, such as the age of the child, gender, the age of the mother, education, and job status, as covariates in the mediation model.

Sample characteristics

There were 169 children their mothers responded to the study surveys. The children’s mean age was 13 (SD = 3.9), while the mothers’ mean age was 41 (SD = 7.1). According to mothers, the children were ranked third in their household. Most mothers (72%) said they lived in rural areas. About 61% of the families had at least three children. Half of the mothers had high school or less education, and more than half were unemployed. Most children were in middle school (72%), see Table  1 .

Study variables description

The mean scores for all scales are presented in Table  2 . The mean score of the video gaming addiction total scale was 61 ± 19.3, indicating a moderate level of addiction. The attention total scale mean was 9 ± 6.50, indicating moderate attention problems. The mean score on the total scale for child memory was 80 ± 31,4, indicating moderate memory issues. Eight subscales of the LEAF had mean scores of 5: factual memory, processing speed, visual-spatial organization, sustained sequential processing, working memory, novel problem-solving, mathematics skills, and written expression skills. These mean scores indicate that a borderline problem exists. However, the mean scores for the comprehension and conceptual learning subscale, attention subscale, and basic reading skills subscale were below five, indicating that there was no problem.

Mediating effect of memory, attention problem on the association between video gaming addiction and cognitive, learning, and academic skills

Video game addiction had a significant impact on attention problems (b = 0.34, p  < 0.001; a1), and child memory (b = 0.18, p  < 0.001; a2). In turn, both attention problems (b = 0.48, p  < 0.001; b1) and child memory (b = 0.38, p  < 0.001; b2) had significant impact on cognitive and learning skills. The results reveal a significant indirect effect of video game addiction on cognitive and learning skills through attention problems (b = 0.17, CI: 0.82, 0.25; c ’ 1). However, there was no significant indirect effect of video game addiction on cognitive and learning skills through child memory (b = 0.07, CI: -0.01, 0.16; c ’ 2). The analysis revealed that confounding variables had no significant effect on the direct or indirect pathways linking video game addiction to cognitive and learning skills. The direct effect of video game addiction on cognitive and learning skills in the presence of the mediators was also found to be significant (b = 0.11, CI: 0.008, 0.401; c ’ -c). Figure  1 displays the mediation analysis findings.

Mediation effect of attention problem and child memory on the association between video gaming addiction and cognitive and learning skills

Previous research has explored the relationship between video game addiction, attention, and memory. Some studies have focused on the relationship between video game addiction and cognitive and learning skills. Others have examined the association between video gaming addiction and all other variables (attention, memory, learning, and cognitive skills). However, no study has explicitly examined the direct and indirect effect of video gaming addiction on learning and cognitive skills through the mediation effect of attention and memory.

This study was done on a sample of Egyptian school children to evaluate the mediation effect of attention and memory on the relationship between video game addiction and cognitive and learning abilities in children. The present study reveals that a gaming addiction can significantly impact attention and memory. This result agrees with Farchakh, Haddad [ 6 ], who conducted a study on a group of Lebanese school children aged 9 to 13 to investigate the association between gaming addiction, attention, memory, cognitive, and learning skills. They found that a greater degree of addiction to video gaming was significantly associated with worse attention scores and worse memory scores. An earlier study suggests that the link between inattention and video game addiction could be described by game genres’ immediate response and reward system. Alrahili, Alreefi [ 2 ] suggest that this may alleviate the boredom typically reported by inattentive users while simultaneously introducing a lack of responsiveness to real-world rewards. Another study on Turkish schoolchildren aged 10 to 16 years old revealed that the total recall scores of the subject group (children who regularly play video games) are significantly lower than those of the control group (children who do not regularly play video games; [ 7 ]).

The current study demonstrates that attention and child memory significantly impacted cognitive and learning skills. This agrees with the opinion of, Gallen, Anguera [ 32 ], who argues that children and young people process information differently, affecting the performance of various cognitive tasks. Additionally, this result disagrees with the findings of Ellah, Achor, and Enemarie [ 26 ], who have stated that students’ working memory has no statistically significant correlation with learning and problem-solving skills. Moreover, their same study showed that different measures of working memory can be attributed to a small variation in low-ability students’ problem-solving skills.

The results revealed a significant indirect effect of video game addiction on cognitive and learning skills through attention. This could be related to the relationship between attention and learning skills. Attention is an essential factor in the learning process because it helps a person make efficient use of data by directing their learning to relevant components and relationships in the input material. If a student can pay attention, they may be able to better retain and understand this material; if not, a lack of attention may lead to difficulties in learning and academic performance. As video gaming addiction affects students’ attention, it may directly affect learning skills [ 33 ]. Another study agrees with the current result, revealing that video game addiction negatively affects adolescents’ learning skills and grade point average [ 34 ].

A child’s memory has an effect on their cognitive and learning skills. Encoding, consolidating, and retrieving experiences and information are the foundation for learning new skills and knowledge [ 35 ]. Video game addiction affects children’s memory. Hence, the expectation is that video game addiction directly affects cognitive and learning skills. However, the present study reveals no significant indirect effect of video game addiction on cognitive and learning skills through child memory. For example, perceptual attention to the exterior world and reflective attention to interior memories need modification of shared representational components in the occipitotemporal cortex. This is shown in episodic memory by recovering an experience from memory, which includes reactivating some of the same sensory areas used during encoding. Furthermore, the prefrontal cortex involves continuous and reflecting attention [ 36 ]. The prefrontal cortex controls memory recall by choosing target memories and filtering or suppressing competing memories [ 36 ].

Another aspect that may be responsible for the absence of a mediating effect of memory on the association between video game addiction and cognitive and learning skills is the presence of the many factors that affect learning and cognitive skills besides memory alone. Life circumstances can affect learning skills rather than memory itself, for example. Problem solving (one of the learning skills) requires a brain that works effectively. Therefore, it is critical to address needs such as physical health, which is influenced by self-care needs such as diet, sleep, and relaxation, as well as children’s social and emotional needs. Furthermore, learning experiences that use all the senses, rather than only hearing or seeing information, result in effective and straightforward information retrieval from memory during problem-solving processes. Such abilities are supposed to be acquired by active participation in learning activities by children [ 37 ]. Finally, long-term focus on online gaming may eventually lead to neglect in learning, leading to a deterioration in learning performance [ 38 ].

Limitations

Our study has some limitations. First, we administered the Clinical Attention Problems Scale only once per student rather than conducting repeated measurements in the morning and afternoon. This approach overlooks potential daytime variations in attention levels, limiting our understanding of each child’s attentional profile. This choice was driven by practical considerations such as reducing the testing burden and participant fatigue. Future research could address this limitation by implementing repeated assessments to comprehend better daytime patterns in children’s attention levels and their implications for learning and behavior. Causality analysis was not possible due to the use of a cross-sectional sample. In addition, some results may be attributable to the small sample size. To fully understand the complex interplay between video game addiction and cognitive outcomes, longitudinal studies and controlled experiments are necessary to provide more conclusive insights into the relationship. It was difficult to include both parents in the study, as most of the fathers said they were too busy to participate. Hence, mothers were the subjects of the study. Certain differences (or lack thereof) are probably artifacts of the sample size. As a result, our findings must be validated by analyzing larger samples. Despite these limitations, this work has the potential to provide insights and open new research avenues.

Implications

Healthcare professionals should be aware of how much children participate in these games and be willing to engage in in-depth conversations with parents about the impact these games may have on children’s health. Therefore, periodical workshops should be held by pediatric and community mental health nurses to enhance student awareness of the effects of video games on their memory, attention, and academic performance. In addition, teaching programs should be held at schools to improve students’ attention, memory, learning, and cognitive skills.

Video game addiction has a significant impact on children’s attention and memory. Both attention and memory have a significant impact on a child’s cognitive and learning skills. These results reveal a significant indirect effect of video game addiction on cognitive and learning skills through attention. However, video game addiction had no significant indirect effect on cognitive and learning skills through child memory. In the presence of the mediators, the direct impact of video game addiction on cognitive and learning skills was also significant.

Data availability

No datasets were generated or analysed during the current study.

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Acknowledgements

The authors extend their heartfelt appreciation and gratitude to all parents who willingly participated in the study.

The authors gratefully acknowledge the funding of the Deanship of Graduate Studies and Scientific Research, Jazan University, Saudi Arabia, through Project Number: GSSRD-24.

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Amani Ali Kappi

Psychiatric and Mental Health Nursing Department, College of Nursing, Mansoura University, Mansoura, Egypt

Rania Rabie El-Etreby & Warda El Shahat Hamed

Pediatric Nursing Department, College of Nursing, Mansoura University, Mansoura, Egypt

Ghada Gamal Badawy & Gawhara Ebrahem

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Amany Ali Kappi contributed to the project by designing the methodology, performing formal analysis, analyzing the data, and writing both the original draft and the manuscript. Rania Rabie El-Etreby contributed to conceptualizing, methodology, conducting, drafting, reviewing, and editing the manuscript. Ghada Gamal Badawy, was responsible for designing, executing, and documenting the investigation, including methodology, and manuscript preparation. Gawhara Ebrahem was responsible for designing, executing, and documenting the investigation, including methodology, and manuscript preparation Warda El Shahat Hamed conceptualized and prepared the methodology and investigation and contributed to writing the original draft. She also reviewed and edited the document. All authors read and approved the final manuscript.

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The researchers obtained approval for this study and data collection in this study from the Research Ethical Committee (REC) of Mansoura University’s Faculty of Nursing (IRB P0506/9/8/2023). All procedures were conducted in accordance with ethical standards outlined by the responsible committee on human experimentation and the Helsinki Declaration of 2008. Consent forms were obtained from all participants. Informed consent was obtained from all the participants in this study (from the mothers of the participant children).

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Kappi, A.A., El-Etreby, R.R., Badawy, G.G. et al. Effects of memory and attention on the association between video game addiction and cognitive/learning skills in children: mediational analysis. BMC Psychol 12 , 364 (2024). https://doi.org/10.1186/s40359-024-01849-9

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Received : 16 April 2024

Accepted : 10 June 2024

Published : 24 June 2024

DOI : https://doi.org/10.1186/s40359-024-01849-9

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