Correlation Between Students’ SAT Scores and Their Family’s Income

Correlation Between Students’ SAT Scores and Their Family’s Income

We can explore the relationship between students’ SAT scores and their family’s income from various perspectives. From a general perspective, we can infer a strong positive correlation between income and SAT scores.

What the correlation tells us

The relationship between the two factors indicates that a family’s income affects the outcome of students’ SAT scores. The positive correlation suggests a higher family income will increase student SAT scores.

Whether the correlation is evidence that having a high family income causes one to have high SAT scores

The correlation is not evidence that high family income causes one to have high SAT scores. There is no objective evidence to prove the claim. Besides, we cannot interpret correlation as causation since there is a difference, as Barrowman (2014) points out.

Whether the correlation is evidence that high SAT scores cause higher income or whether it tells us something else

The correlation is not evidence that high SAT scores cause higher income. This is an inverted interpretation of the initial claim, which makes the family’s income a dependent variable and SAT scores an independent variable. Initially, the dependent variable is SAT scores, while the independent variable is the family’s income. Although, logically, higher SAT scores may lead to better jobs and higher family incomes, the correlation does not evidence this.

Why correlation alone is rarely sufficient to demonstrate cause.

Correlation shows that two variables are related and can occur together. However, this does not imply that one of these events is the cause of the other. Although a causal relationship might exist between the variables, correlation does not provide any information about causation, as Turner (2011) suggests.

Personal examples of two variables that may be correlated but do not have a cause-and-effect relationship and the type of bivariate correlation involved based on the measurement scales of the variables.

We can say that the crime rates measured by the number of crimes in a community are negatively correlated with the average level of education measured by school years. This correlation does not necessarily mean causation since the intermediate level of education might not cause a decrease in crimes.

Reference

Barrowman, N. (2014). Correlation, Causation, and Confusion. The New Atlantis, 43, 23–44. http://www.jstor.org/stable/43551404

Tanner, D. (2011). Statistics for the Behavioral & Social Sciences. Bridgepoint Education Inc.

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Before beginning work on this discussion forum, read Chapter 8 in the course textbook and the Instructor Guidance for Week 5. Review the correlation Doesn’t Equal Causation: Crash Course Statistics #8 links to an external site. And The Danger of Mixing Up Causality and Correlation: Ionica Smeets at TEDxDelftLinks to an external site. Videos. In this post, you will be challenged to examine how statistical tests, such as correlation, are commonly used and the possible limitations of such analyses. Additionally, you will need to explain statistical concepts, accurately interpret the results of statistical tests, and assess assumptions, limitations, and implications associated with statistical tests.

Much has been written about the relationship between students’ SAT scores and their family’s income. Generally speaking, income and SAT scores have a strong positive correlation. Consider and discuss the following questions as you respond:

What does this correlation tell you?
Is this correlation evidence that having a high family income causes one to have high SAT scores?
Is this correlation evidence that high SAT scores cause higher income? Or does this tell you something else? Explain your answer.
Explain why correlation alone is rarely sufficient to demonstrate cause.
Provide an example of two variables that may be correlated but do not have a cause-and-effect relationship. Identify what type of bivariate correlation is involved based on the measurement scales of the variables.