Answer the following questions: (5 points)
1. What is the definition of a correlation and why would a researcher be interested in using this type of analysis?
2. What is another name for a Positive relationship and a Negative relationship?
3. Describe the association between two variables when the relationship is Negative.
4. Is a correlation a good way to determine cause-and-effect? Why or why not?
5. When a large number of cases are examined and a positive relationship is found, what else should one expect to find?
Conduct a correlational analysis on the following example (use datafile: Assignment 3 Data.sav) (5 points)
Example: A school psychologist is interested in determining if test anxiety is affecting her students’ performance on their exams. She randomly selected 103 students from her school and conducted a correlational analysis to try and answer her question. She hypothesized that as anxiety increases, test performance decreases.
Based on this example answer the following questions: (5 points)
1. Why is a correlational analysis the most appropriate technique to test her hypothesis?
2. Use the data set provided and conduct a Bivariate Correlational Analysis using SPSS.
Hint: In the Bivariate Correlations dialogue box in SPSS, select Pearson. Create a Simple Scatterplot with Exam Performance on the Y-axis and Exam Anxiety on the X-axis.
3. Observe and briefly explain the trend seen in the Scatterplot (1-2 sentences).
4. What is the strength and direction of the relationship between Performance and Anxiety?
5. Based on these findings, can she infer that one variable Causes the other (i.e., cause-and-effect)? Why or why not?
6. Discuss the findings using Morgan et al. (2002) pp. 33-34.
Provide examples of the following using variables and a made up correlation to illustrate your point: (5 points)
a. Strong positive (direct) correlation
Construct your response like the example given here: A strong positive correlation exists between study time and GPA (r = .74). That is, as study time increases so does GPA.
b. Weak positive correlation
c. Strong negative (inverse) correlation
d. Weak negative correlation
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