Correlation and Causation
Major Concept Summary: Correlation and Causation
ED 304: Ed Psych and Human Development
Author: Lahna McClaine
Verified by: Abbie Merritt 02/24/2023,
Disclosure: ChatGPT3 was used in the creation of this resource.
Connections to Education:
Correlation and causation can connect to education in several ways:
- Identifying patterns in student performance: By analyzing data on student performance, teachers can identify patterns and correlations between different variables such as attendance, homework completion, and test scores. This can help identify factors that may be related to academic success or failure.
- Developing effective instructional strategies: By understanding the cause-and-effect relationship between different variables, teachers can design instructional strategies that are most effective in promoting student learning.
- Identifying and addressing barriers to learning: By understanding the correlation and causality between different variables, teachers can identify and address barriers to learning that may be impacting student performance.
- Evaluation of educational programs: Understanding correlation and causation can help educators evaluate the effectiveness of educational programs and interventions, by identifying the factors that contribute to positive outcomes and making adjustments as needed.
- Understanding student behavior: Understanding correlation and causation can help understand student’s behavior and motivation.
Overall, understanding correlation and causation can help educators to make more informed decisions about teaching strategies and to design more effective instructional methods that are tailored to the needs of their students.
Summary:
Correlation refers to the relationship between two variables, where a change in one variable is associated with a change in another variable. This relationship can be positive, meaning that as one variable increases, the other variable also increases, or negative, meaning that as one variable increases, the other variable decreases. Correlation can be measured using a statistic called the correlation coefficient, which ranges from -1 to 1, with -1 indicating a perfect negative correlation, 0 indicating no correlation, and 1 indicating a perfect positive correlation. For example, there may be a correlation between a child’s age and their height, where older children tend to be taller. However, correlation does not prove causation, which is the relationship between an event (the cause) and a second event (the effect), where the second event is a result of the first.
Causation can be established through experimental research, where a cause-and-effect relationship is tested through manipulation and control of variables. It refers to a causal relationship between two variables, where a change in one variable directly causes a change in the other variable. For example, in a study on the effects of nutrition on cognitive development, children in a control group would be given a normal diet, while children in an experimental group would be given a special diet. If the children in the experimental group showed significant improvements in cognitive development compared to the control group, it can be inferred that the special diet caused the improvement.
Causation can be determined through experimental designs, such as randomized controlled trials, where a group of individuals is randomly assigned to a treatment or control group, and the effects of the treatment or control group, and the effects of the treatment are observed. This allows for the establishment of a cause-and-effect relationship between the variables of interest.
It is important to note that correlation doesn't imply causation. Just because two variables are correlated, it does not mean that one causes the other.
In human development, correlation and causation are important concepts to understand as they help researchers and practitioners understand the underlying mechanisms that influence development. For example, studying the correlation between parenting styles and child outcomes can help us understand the relationship between the two, but it does not prove that one causes the other. Further research is needed to establish causality.
In summary, correlation and causation are two different and important concepts in human development research, where correlation describes the relationship between variables while causation describes the relationship between cause and effect. Establishing causality requires experimental research, while correlation can be studied through observational studies. In education research, it is important to be mindful of the complexities of correlation and causation and to carefully consider other factors that may be affecting the relationship between variables. It is important to understand the difference between the two and not confuse correlation with causation. Additionally, there can be a causal relationship between two variables without a correlation. So it is important to consider other factors before drawing any conclusions.
Resources:
- https://www.abs.gov.au/websitedbs/D3310114.nsf/home/statistical+language+-+correlation+and+causation
- https://www.jmp.com/en_us/statistics-knowledge-portal/what-is-correlation/correlation-vs-causation.html
- https://www.understandinghealthresearch.org/useful-information/correlation-and-causation-15
Ask the Teacher: Interviewed Brother Benitez
Why is it useful?
Correlation and causation are important for teachers because they help to establish the relationship between different variables and how they affect learning outcomes.
Correlation can help teachers to identify patterns in students’ performance and to identify factors that may be related to academic success or failure. For example, if a teacher notices a correlation between students’ attendance and their test scores, they may decide to focus on strategies to improve attendance. Causation, on the other hand, helps to establish the cause-and-effect relationship between different variables and can help teachers identify the most effective teaching strategies.
By understanding correlation and causation, teachers can make more informed decisions about teaching strategies and can design more effective instructional methods that are tailored to the needs of their students. Additionally, understanding correlation and causation can also help teachers identify and control for confounding variables that may be impacting student performance, which can be useful for designing interventions and evaluating the impact of these interventions.
How do they use it?
Teachers will often use correlation and causation thinking when they give out assessments, homework, assignments (anything including a score), and even for trying new methods. They pay attention to patterns in the entire class as well as any patterns that might come from individual students. For more experienced teachers it is easier to realize what is correlated and what might be the cause. They know that many more factors play a part in finding problems and solutions. Teachers who teach by semester instead of the school year might try to experiment with different ideas between semesters and find what works best for the students or which shows the best results.
Current events in Education:
Above is an article that talks about the problems that have arisen from COVID-19 including vaccines in Kindergarteners. It states that many new kindergarteners don’t have their vaccines because COVID disrupted the pattern and routine of getting and completing shots. This story shows a correlation but there is no clear evidence that in reality, COVID is the cause for why kids aren’t getting their vaccines before entering kindergarten.
Vocabulary:
- Comparison: a consideration or estimate of the similarities or dissimilarities between two things or people.
- Relationship (cause and effect): how two or more concepts, objects, or people are connected, or the state of being connected.
- Independent Variable: the variable in an experiment whose variation does not depend on that of another
- Dependent variable: The variable that is being measured or observed in an experiment, and its value is expected to change as a result of the manipulation of the independent variable.
- Confounder: a variable that is associated with both the independent and dependent variables and could be responsible for the observed relationship.
- Control: a group in an experiment that is used as a reference point to compare the results of the experimental group.
Key Thinkers:
Many key thinkers have contributed to our understanding of correlation and causation. Some notable figures include:
- Sir Ronald A. Fisher, a statistician and geneticist, who developed many statistical methods for analyzing data and established the concept of the null hypothesis. (The null hypothesis is a characteristic arithmetic theory suggesting that no statistical relationship and significance exists in a set of given, single, observed variables between two sets of observed data and measured phenomena.)
- Jerzy Neyman, a statistician, developed the concept of a confidence interval and emphasized the importance of statistical inference.
- Bradford Hill, an epidemiologist, developed the “Hill’s criteria” for establishing causality, which includes the strength of association, consistency, temporality, biological gradient, specificity, coherence, experiment, and analogy.
- Pearl Judea, a computer scientist, who developed the theory of causal inference, which uses graphical models to represent causal relationships and uses these models to identify and infer causality.
- David Hume, a philosopher, who argued that causality cannot be inferred solely from observations and that our belief in causality is based on habit and custom.
Summary Box:
Correlation refers to a relationship between two variables, where they tend to change in the same direction. Causation refers to a relationship where one variable causes a change in another variable. It’s important to note that just because two variables are correlated, it doesn’t necessarily mean that one causes the other. To establish causation, you need to establish a temporal relationship, where the cause comes before the effect, and you need to control for alternative explanations. Additionally, a correlation coefficient can be used to measure the strength of the correlation between two variables, but it doesn’t indicate the direction of the relationship.
Warning Box:
A common disagreement about correlation and causation is the belief that correlation implies causation. Just because two variables are correlated does not mean that one variable causes the other. There may be other factors that are responsible for the relationship, or it could be a spurious correlation, where the relationship is coincidental and not causal. Additionally, it’s important to note that correlation does not imply causation in both directions. Just because variable A is correlated with variable B, it does not mean that variable B causes variable A. Therefore, it is important to use other methods such as experimental design to establish causality.
Media:
Tedtalk: https://www.youtube.com/watch?v=8B271L3NtAw
Explanation video: https://www.youtube.com/watch?v=U-_f8RQIIiw
Quiz Questions:
Which of thew following statements best describes the difference between correlation and causation?
Correlation is when two variables are related, while causation is when one variable causes the other.
Correlation is when one variable causes the other, while causation is when two variables are related
Correlation and causation are the same thing
If a researcher wants to determine causation, which of the following is an important factor to consider?
The strength of the correlation between two variables
The direction of the correlation between two variables
The presence of a plausible mechanism that could explain how one variable could cause the other
True or False: A study can only establish causation if the researcher manipulates one of the variables.
True
False
A causual relationship between two variables where a change in one variable directly causes a change in the other variable is known as _______________.
Correlation
Causation
Coincidence
Experiment
Just because two variables are ___________, it does not mean that one causes the other.
Causal
Connected
Correlated
Coincidental
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