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Correlation Worksheet PSYCH/625 Version 5

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University of Phoenix Material Correlation Worksheet Complete the questions below in at least 90 words each. Be specific and provide examples when relevant. Cite any sources according to APA guidelines. Question

Answer

Draw a scatterplot of each of the following:    

A strong positive correlation A strong negative correlation A weak positive correlation A weak negative correlation

Give a realistic example of each.

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Correlation Worksheet PSYCH/625 Version 5

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Jackson,S. (2017), “If height and weight were perfectly correlated (+1.00) in a group of 20 people, this would mean that the person with the highest weight would also be the tallest person, the person with the second-highest weight would be the secondtallest person, and so on down the line” ( p. 256). A weak positive correlation can be the purchase of a candy bar, the more candy bars you buy, the more the amount of money increases. It can be strong or weak if you are charging very little for the candy bars.

According to Jackson (2017), an inverse relationshipis one in which an increase in one variable makes a decrease on the other; this is also called a negative correlation. An example of this would be the amount of sleep a human being needs depending on age, as we get older we need less sleep. This would be known as a negative correlation (a weak negative correlation). A strong negative correlation would be the older you get, the more your visual acuity decreases. . What is the meaning of the p value of a correlation coefficient? Why is it important to know the amount of shared variance when interpreting both the significance and the meaningfulness of a correlation coefficient?

The p-value is a way to calculate the meaningfulness of an experimental test and the significance of the results of the test being conducted. With correlations, it is important to understand the magnitude of the common variance between variables. We use the significance or p-value to determine the meaningfulness of a correlation coefficient to see if the variables are correlated (Jackson, 2017). Is important to know the amount of shared variance because only the shared variance is able to tell us if the numerical correlation coefficient value is truly reflective of shared variance; hence, it lets you know how much variance in one variable is accounted for by the variance in the other variable (Jackson, 2017). The amount of shared variance also lets you know how strong the relationshiop is (e.g., an (r) of .9 has a

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Correlation Worksheet PSYCH/625 Version 5

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shared variance of 80% and an (r ) of .3 has a shared variance of 9%. “It is also important to know that even though the correlations between the variables may be strong it may also be that the relationship is the result of some third variable that influences both of the measured variables” (Jackson, 2017, p.318).

If a researcher wanted to predict how well a student might do in college, what variables do you think he or she might examine? What statistical procedure would he or she use?

If one wants to know if an individual is likely to succeed in college, one can use multiple regression analysis since there can be more than one independent variable (x-axis variable) even though there is one dependent variable (y-axis variable) (Jackson, 2017). For example, one can use standardized test scores like ACT or SAT; in addition, one can use whether he or she attended public or private high school or whether that individual did not attend high school and instead got his or her GED. In addition, another variable that can be use is if he or she came from a household that makes a high income or a low income.

What does the saying “correlation does not imply causation” mean? According to Jackson (2017), “The most common error made when interpreting correlations is assuming that the relationship observed is causal in nature— that a change in variable A causes a change in variable B. Correlations simply identify relationships— they do not indicate causality” (p. 268). In other words, correlation between two events or variables simply indicates that a relationship exists, whereas causation says that one event actually causes the other (Jackson, 2017). When we say that correlation does not imply causation, we mean that just because you can see a connection or relationship between two variables, doesn't mean that one causes the other. Although one variable may cause the other, it can not be determined by looking at the correlation alone. The variance could be due to a third unidentified variable. More research testing would need to be completed before causation can be determined (Jackson, 2017). How does determining a best-fitting line help us to predict from one variable to

According to Jackson (2017), the best-fitting line will be useful when one needs to know how strong of a

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Correlation Worksheet PSYCH/625 Version 5 another?

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linear relationship does one independent variable (xaxis variable) corresponds with one dependent variable (y- axis variable) (Jackson, 2017). If most of the data points fall on the line or close to the line, this means the data has a moderate to strong linear relationship. However, if a few points or none of the points fall on the best-fitting line, this means that the data does not show a strong linear relationship; the relationship could be curvilinear (Jackson, 2017).

Reference Jackson, S.L. (2017). Statistics plain and simple (4th ed.). Boston, MA: Cengage.

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