Chi-Square and Correlation- Classmate Response (2): Topic 4 DQ 1

Chi-Square and Correlation- Classmate Response (2): Topic 4 DQ 1
QUESTION-Correlation is a common statistic to measure a general linear relationship between two variables. Explain why correlation does not equal causation.
Classmate (Samantha)-. Response to the question-
Correlation and causation are always heard like two peas in a pod. Sometimes I hear them used as correspondents and sometimes not. Correlations between variables show us that there is a pattern in the data: that the variables we have tend to move together. However, correlations alone don’t show us whether or not the data are moving together because one variable causes the other(JMP 2021). Even if there is a correlation between two variables, we cannot conclude that one variable causes a change in the other. This relationship could be coincidental, or a third factor may be causing both variables to change(Khan Academy 2021). an example of this would be when doing research on psychiatric medications. How do we know that the medications are what is affecting the imbalance of our thoughts and our feelings? There are also some other factors that could contribute to the side effects like not having enough nutritious vitamins as well as feeling stressed out. I always think about this when I used to work in a behavioral hospital. Then I would try to figure out ways to help the patients non-medicinally like doing yoga or going out on the patio or creating fun activities for the patients and their peers to release Seratonin. It helped alongside the medication which was a great outcome for their care.
Resources
Khan Academy. (n.d.). Correlation and causation | Lesson (article). Khan Academy. Retrieved October 3, 2021, from https://www.khanacademy.org/test-prep/praxis-math/praxis-math-lessons/gtp–praxis-math–lessons–statistics-and-probability/a/gtp–praxis-math–article–correlation-and-causation–lesson.
Correlation vs causation. JMP. (n.d.). Retrieved October 3, 2021, from https://www.jmp.com/en_us/statistics-knowledge-portal/what-is-correlation/correlation-vs-causation.html.
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Solution
Response
Hello, thank you for granting me this opportunity to respond to your post. I appreciate your interest in taking part in the discussion and contributing wholeheartedly. I will only add a few remarks to your post. Correlation tends to measure a relation between one variable over the other one. The variable can take either a positive or negative direction. Based on correlated data, the degree of one variable is related to change on another variable, which occurs in either positive or negative direction of correlation (Schober et al., 2018).
In addition to your points regarding the correlation does not equal causation, I would like to point out that it is crucial to note; an observed relationship between two variables does not determine any causal relationship. For instance, the coffee business tends to be on higher levels during cold and rainy seasons, thus increasing the sales, but buying umbrellas does not necessarily lead to high sales of coffee. Thus showing a relation between two variables takes different correlations (Schober et al., 2018). Therefore, causation exists, though it still does not mean that correlation determines causation.
Correlation and causation can be a little bit confusing to so many people and even to the researchers. The main aim is to focus on observational data and understand the relation between variables before making assumptions (Rohrer, 2018). Based on different research scenarios, a researcher should examine the various factors showing any association in the study, especially if it is a concept that involves observation and statistical testing. Categorizing the variables is also vital in proving the relationship of causation if the existing relation is on both variables or just one. Highlighting such concepts can help reduce the confusion between correlation and causation. Furthermore, a clear application of both terms can improve the quality of various research work that uses the terms interchangeably.
References
Rohrer, J. M. (2018). Thinking clearly about correlations and causation: Graphical causal models for observational data. Advances in Methods and Practices in Psychological Science, 1(1), 27-42. https://journals.sagepub.com/doi/pdf/10.1177/2515245917745629
Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation coefficients: appropriate use and interpretation. Anesthesia & Analgesia, 126(5), 1763-1768. https://journals.lww.com/anesthesia-analgesia/Fulltext/2018/05000/Correlation_Coefficients___Appropriate_Use_and.50.aspx