Discussion 6 | Correlation vs. Causation
Initial discussion post is to be 220-250 word mimum and 2 cite scholarly resources
Peer reply response to two peers
150-160 word mimum for each post reply to peer stated below. One response should have 1 citation scholarly resource
REVIEW
Review all assigned reading and resources in preparation for the discussion.
Correlation Doesn’t Equal Causation: Crash Course Statistics #8 [00:12:07] (opens in a new window, link is below).
https://youtube.com/watch?v=GtV-VYdNt_g%3Frel%3D0%26autoplay%3D1%26iv_load_policy%3D3%26start%3D0
Correlation is a statistical measure that calculates the strength of the relationship between the relative movements of the two variables. Causation is the act or process of causing something to happen or exist. In other words, causation means one event is 100% certain to cause something else. Another important concept is the confounding variable; a third variable. This is an outside influence that changes the effect of one variable’s relationship over another. This extraneous influence is used to influence the outcome of an experimental design. Simply, a confounding variable is an extra variable that has entered the equation but was not accounted for. For example: “as murder rates rise, so does the sale of ice cream”. This statement comes with three possibilities:
Possibility #1: Murders cause people to purchase ice cream.
Possibility #2: Purchasing ice cream causes people to murder or get murdered.
Possibility #3: A confounding variable (third variable) which causes the increase in BOTH ice cream sales AND murder rates. For example, the weather. When the weather is cold (winter conditions), people stay at home rather than go outside and murder people. Also, they probably don’t eat a lot of ice cream. When the weather is hot (summer conditions) people spend more time outside interacting with each other, and are more likely to get into the kinds of situations that lead to murder. Also, they are probably buying more ice cream! In this example, the weather is a variable that confounds the relationship between ice cream sales and murder rates.
RESPOND
Why is the statement “correlation is not causation” important to statistical analysis? Share at least 2 examples to illustrate this point.
Using your examples, what are the consequences to the outcome if this statement is ignored?
Share an example of a confounding variable. How do confounding variables affect the research process and/or outcome?
Requirements
Initial posts: 150-200 words
DISCUSS
Post at least two substantive responses to your colleagues. Responses could include suggestions for further resources, questions of clarification, or providing context and insight. Avoid simple posts of agreement; if you agree, explain why, and then thoughtfully further the conversation. It is important to include at least a scholarly resource and provide valid examples as needed.
Requirements
Response to Peers:
150 minimum words
Respond to a minimum of two posts
APA formatted sources where specified
Please respond to peers below
Peer 1
Yurell Kellem:
Correlation is not causation, according to statistical analysis, because the two terms have different meanings in data gathering and analysis (CrashCourse, 2018). Correlation, for example, is a strategy that helps people to recognize a pattern between data. As a result, correlation merely depicts the size of the relationship between variables as well as how the two change over time (Gogtay & Thatte, 2017). Correlation, on the other hand, does not prove causation because causation goes a step further in the relationship. Individuals can use causality to determine whether variables are flowing in the same direction (Rohrer, 2018). As a result, causality confirms that variables are moving together in data analysis. For example, one could misinterpret the two by claiming that watching a certain movie causes more fatalities or that buying an air conditioner causes more births (Shipley, 2016). When people disregard the preceding statement, it can lead to misinterpretation of the information provided. Another variable, a confounding variable, could be to blame for the outcomes indicated. Confounding variables, for example, influence the outcome by revealing what causes high death and birth rates in each case.
References
CrashCourse (2018, March, 15). Correlation Doesn’t Equal Causation: Crash Course Statistics [Video]. YouTube. https://www.youtube.com/embed/GtV-VYdNt_g?rel=0&autoplay=1&iv_load_policy=3&start=0
Gogtay, N. J., & Thatte, U. M. (2017). Principles of correlation analysis. Journal of the Association of Physicians of India, 65(3), 78-81.
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.
Shipley, B. (2016). Cause and correlation in biology: a user’s guide to path analysis, structural equations and causal inference with R. Cambridge University Press.
Peer 2
Kyler Keef:
Correlation and causation can easily work hand in hand in statistical analysis. In the statement “correlation is not causation”, this reflects that because of a carrying out of an action, does not entirely mean that it is the strength of the relationship of why it has happened. If ignored, it can illustrate some consequences. One example would be if you have no money you are more likely to get out and go shopping. Most people buy on credit today and pay it back later whenever they have the money. The consequence to this is that if you continue to pay before you get the money, one day you might not get that money to pay it back and therefore get in to some financial troubles. Another example is if you have low social media followers, you are going to try more and put more effort in to promoting yourself. If you have a good fanbase, then you don’t have to worry about this therefore resulting in some poor posts and responses from people who already have a good following. A third variable can be a cofounding variable and it can change an effect, such as your car breaking down while going out to shop. While it may not have a direct effect on the money situation, it does place a direct effect on shopping because it is a preventable method from keeping outside and doing what you want to do. They can affect the outcome for this same reason as well
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