Why do we do the things we do? What makes us tick? A huge part of understanding human behavior is figuring out cause-and-effect, or what’s known as a causal relationship.
If we can pinpoint the reasons behind certain thoughts, feelings, and actions, we’re better equipped to understand ourselves and others. And that’s where psychology comes in.
It’s easy to assume that because two things happen together, one caused the other. But often, that’s not the case. Just because things are correlated doesn’t mean there’s a direct causal relationship.
This outline will take a closer look at the complexities of causal relationships in psychology: how we can identify them, and how they apply to our understanding of the human mind.
It’s not always easy to figure out what causes what. There can be confounding variables (hidden factors influencing the results) and directionality problems (not knowing which thing came first). But understanding these challenges is key to drawing accurate conclusions about why we behave the way we do.
What Is Causation?
In psychology, a causal relationship means that one event directly causes another. If A causes B, then a change in A will directly produce a change in B. It’s a pretty straightforward concept, but it’s also one that’s easily misunderstood. Here’s an example:
If you consume a toxic substance, that will cause you to become ill. The toxic substance is the cause, and the illness is the effect.
Correlation vs. Causation
Here’s where it gets a little sticky. Correlation is an association or relationship between two or more things. A correlation can be positive, meaning that as one thing increases, the other one increases too. Or a correlation can be negative, meaning that as one thing increases, the other one decreases.
The important thing to remember is that just because two things are related doesn’t mean that one causes the other. Correlation does not equal causation!
Sometimes you’ll hear about something called a “spurious correlation.” These are correlations that seem to imply a relationship, but that are really just coincidences, or that are caused by some hidden third factor. For example, there’s a correlation between ice cream sales and crime rates: As ice cream sales go up, so do crime rates. Does this mean that ice cream causes crime? Of course not! It’s more likely that both ice cream sales and crime rates go up during the summer months.
Types of causality
You’ll find that the concept of causality is more complex than you might think. Here’s a rundown of the different types of causality that psychologists consider when they’re looking at the relationships between phenomena.
Direct causality
This is the simplest type of causality. It means that one variable directly influences another without any intermediary variables coming into play. For example, researchers have found that a particular gene may directly cause a higher risk of a specific disease.
Indirect causality
In this type of causality, one variable influences another, but it does so through one or more intermediary variables. Think of it this way: Stress (variable 1) can lead to a poor diet (the intermediary variable), which then leads to weight gain (variable 2).
Necessary and sufficient causes
A necessary cause is a condition that must be present for an effect to occur, but it doesn’t guarantee that the effect will occur on its own. For example, exposure to a virus is necessary for contracting a viral disease, but not everyone who’s exposed to the virus gets sick.
On the other hand, a sufficient cause is a condition that guarantees that an effect will occur. For example, decapitation is a sufficient cause of death.
Contributory cause
This is a condition that increases the probability of an effect occurring. For example, smoking is a contributory cause of lung cancer. This doesn’t mean that everyone who smokes will get lung cancer, but it does mean that smoking increases your risk of developing the disease.
Identifying causal relationships: Challenges and solutions
It’s easy to fall into the trap of thinking that if two things seem to be linked, one is causing the other. But figuring out if a relationship is truly causal is trickier than you might think. Here are some of the common pitfalls and how researchers try to avoid them.
Challenges in establishing causality
- The third variable problem. Just because two things seem to happen together doesn’t mean one is causing the other. There could be a third, unmeasured variable at play. For example, ice cream sales and crime rates might rise together in the summer, but that doesn’t mean ice cream causes crime. A third variable, like hot weather, could be the reason for both. These “confounding variables” can make it look like there’s a causal relationship when there isn’t one.
- The directionality problem. Even if you know two things are related, it can be hard to tell which one is causing the other. Does stress cause insomnia, or does insomnia cause stress? It could go either way, or they could be feeding into each other in a cycle. This “reverse causation” makes it tough to pinpoint the true cause.
- Regression to the mean. Extreme values tend to move closer to the average over time. Imagine someone scores exceptionally high on a test; their next score is likely to be lower, simply because it’s unlikely they’ll perform at their absolute best again. This natural fluctuation can be mistaken for a causal effect if you’re not careful.
Methods for determining causation
So, how do researchers actually figure out if one thing causes another? Here are some key strategies:
- Experimental designs. The gold standard for establishing causality is the experimental design. This involves manipulating one variable (the independent variable) and measuring its effect on another (the dependent variable).
- Control groups and random assignment. To make sure the effect you’re seeing is actually due to the independent variable, you need a control group that doesn’t receive the manipulation. Participants should also be randomly assigned to either the experimental or control group to minimize any pre-existing differences between them.
- Hypothesis testing and A/B/n experimentation. Researchers use hypothesis testing to determine if the results of an experiment are statistically significant, meaning they’re unlikely to be due to chance. In fields like marketing and web design, A/B testing (or A/B/n testing with multiple variations) is a common way to test if changes to a website or ad campaign actually lead to a desired outcome.
- Repeatability, controlled variables, and statistical analysis. Identifying causation requires careful research, repeatability, controlled variables, and statistical analysis.
Research designs and validity
One of the biggest challenges in psychology is figuring out what causes what. Did a particular therapy cause a reduction in anxiety, or did the anxiety lessen on its own? Did a new teaching method cause students to perform better, or were there other factors at play? To answer these questions, psychologists rely on different research designs, each with its own strengths and weaknesses.
Correlational research designs
Correlational research can be helpful for identifying relationships between variables. For example, we might find a correlation between hours of sleep and test scores – people who sleep more tend to score higher. However, correlation does not equal causation. Just because two things are related doesn’t mean one causes the other. There could be other factors involved, like a person’s overall health habits or their natural aptitude for the subject.
These “other factors” are called confounding variables, and they can make it tricky to draw conclusions about cause and effect from correlational studies.
Experimental research designs
To really nail down cause and effect, psychologists use experimental designs. The key here is manipulation. Researchers deliberately change one variable (the independent variable) to see how it affects another variable (the dependent variable). They also try to control any other factors that could influence the outcome (extraneous variables).
For example, a researcher might manipulate whether or not participants receive a new medication (independent variable) and then measure their anxiety levels (dependent variable). By carefully controlling other factors, like participants’ diets and exercise habits, the researcher can be more confident that any changes in anxiety are actually due to the medication.
Two key concepts in experimental design are internal and external validity. Internal validity refers to how confident we are that the experiment actually shows a true causal relationship. External validity refers to whether the results can be generalized to other people and situations.
Causal models and theories in psychology
In psychology, a causal relationship means that one thing (an action, event, or situation) produces another. For example, if you consistently deprive a lab rat of sleep, that deprivation causes cognitive impairment. The sleep deprivation is the cause, and the cognitive impairment is the effect. A causal relationship can also be referred to as cause and effect.
Causal models are tools that psychologists use to represent the causal relationships they believe exist between variables. These models can take various forms, such as path diagrams, which use arrows to show the direction of influence between variables, or structural equation models, which are more complex and can test the fit of a theoretical model to observed data.
Psychological theories are, at their core, explanations of how the mind works and why people behave the way they do. Many of these theories propose causal relationships:
- Attribution theory describes how people explain the causes of events and behaviors. It suggests that we’re constantly trying to figure out why things happen, attributing them to either internal factors (like personality) or external factors (like the situation).
- Social Cognitive Theory emphasizes that individuals learn by watching others and modeling their behavior. This theory suggests that exposure to certain behaviors (the cause) can lead to the adoption of similar behaviors (the effect).
- Cognitive Dissonance Theory focuses on how conflicting beliefs create psychological discomfort. To resolve this discomfort, people may change their beliefs or behaviors. The conflicting beliefs are the cause, and the change in attitude or behavior is the effect.
Two important concepts related to causal relationships are mediation and moderation.
Mediation occurs when the effect of one variable on another is explained by a third variable. For example, imagine that childhood trauma causes depression. But in reality, the trauma causes low self-esteem, which in turn causes depression. Low self-esteem mediates the relationship between trauma and depression.
Moderation happens when the relationship between two variables depends on the level of a third variable. Let’s say that exercise reduces anxiety, but only for people who are highly motivated. Motivation moderates the relationship between exercise and anxiety. Exercise only reduces anxiety when motivation is high.
APPLICATIONS OF CAUSAL REASONING IN PSYCHOLOGY
Causal reasoning is a cornerstone of psychological research, influencing our understanding and treatment of a wide range of human experiences. Here are some key applications:
- Clinical Psychology: Figuring out the underlying causes of mental disorders is crucial to creating effective treatments. This involves identifying the factors that contribute to conditions like depression and anxiety, and then designing interventions based on solid models of how behavior changes.
- Social Psychology: Social psychologists delve into the reasons behind our social behaviors and attitudes. They investigate what makes people prejudiced, aggressive, or helpful, seeking to understand the causal chains that lead to these behaviors.
- Developmental Psychology: Understanding how children develop is a major focus of psychology. Researchers examine how parenting styles, early life experiences, and environmental factors have a lasting impact on a child’s growth and well-being.
- Cognitive Psychology: This area explores the mechanisms that drive our cognitive processes. Researchers investigate the neural and cognitive causes of essential functions like memory, attention, and how we make decisions.
Frequently Asked Questions
What is an example of causality in psychology?
Let’s say researchers find that increased screen time is associated with lower academic performance in children. Demonstrating causality, however, requires more than just observing this correlation. To establish that screen time causes lower grades, psychologists would need to conduct controlled experiments. For instance, they might assign one group of children to limit their screen time and another group to continue with their usual habits. If, over time, the group with limited screen time consistently achieves higher grades than the control group, researchers can more confidently infer a causal relationship. It’s important to note that many factors can influence academic performance, so isolating screen time as the sole cause is often difficult.
What is a causal relationship in psychology?
In psychology, a causal relationship exists when a change in one variable (the independent variable) directly causes a change in another variable (the dependent variable). This means that the independent variable is the reason why the dependent variable changes. Establishing causality requires careful research design to rule out other potential explanations. For instance, let’s say a study shows that mindfulness meditation reduces anxiety. To claim a causal relationship, researchers would need to demonstrate that the meditation itself is responsible for the reduction in anxiety, and not some other factor, like the participants’ expectations or a pre-existing tendency toward relaxation. Random assignment to conditions and control groups are crucial elements in establishing causal links.
Closing Thoughts
Establishing causal relationships in psychology is tough, because it’s so difficult to rule out all the other potential factors that could be influencing the outcome. You have to be so careful about confounding variables.
It’s essential to remember that correlation does not equal causation, whether you’re reading research or applying psychological principles in the real world. We need well-designed studies and sophisticated statistical tools to have any hope of figuring out what’s really causing what.