Question 1

Michelle Steward is a marketing professor at Wake Forest University. Michelle had been asked by the administration to study a sample of classes at Wake to help the university understand the student population better particularly in terms of factors that differentiate students with high versus low GPAs. One of the questions asked was: "What score did you earn (0 to 100)on the last test that you took?" and another question in the study asked "How much time, estimated in numbers of minutes, did you study for the last test you took?" Michelle decided to run a Pearson Product Moment correlation analysis on these two questions. When she did, SPSS generated the following output: Pearson Correlation .98; Sig. (2 tailed).0001. Michelle knew that this meant:
A) there was a significant, nonmonotonic association between the two variables
B) there was the presence of an association because the probability of supporting the alternative hypothesis is very low, less than 1 percent
C) there was the presence of a negative association; the probability of supporting the null hypothesis that there is no association is only .01 percent
D) there was the presence (aka "significant") of a positive, "very strong" association between the variables
E) None of the above; Michelle should not have run a Pearson Product Moment correlation because the two variables are both categorical (aka nominal).

Accepted Answer

A Pearson Product Moment correlation analysis measures the strength and direction of a linear relationship between two continuous variables. The output of .98 indicates a positive, strong correlation between the score earned on the last test and the amount of time spent studying for the test. The significance level of .0001 indicates that the probability of obtaining such a result by chance alone is less than 1%, which supports the alternative hypothesis that there is a non-zero correlation between the two variables. Therefore, there is a strong, positive association between the two variables.

Question 2

If you plotted data between two variables and the points all fell precisely in a straight line that was sloping upward to the right, your correlation coefficient would be equal to +1.0.

Accepted Answer

A correlation coefficient of +1.0 indicates a perfect positive correlation, meaning that as one variable increases, the other variable increases proportionally, and the points would fall precisely in a straight line sloping upward to the right.

Question 3

The advertising director in your firm announced her resignation this morning to take another job, and she is leaving this afternoon. Your boss has asked you to take charge of advertising. Unfortunately, you learn the former director was just beginning planning for an upcoming promotion of one of the company's new products. Your immediate decision is to determine a brand name for the product. As the former director leaves, she stops by to drop off some marketing research reports she had just received, which includes several tests on brand names that were proposed for the new brand. The research company tested 30 potential brand names. For each brand name, they collected data on a number of variables such as "intention to purchase" and "attitude toward the brand name." All these variables were collected using 5-point intensity continuum scales. Thus, all the variables possess an interval level of measurement. Just focusing on the two variables mentioned ("intention to purchase" and "attitude toward the brand name"), what type of analysis would you conduct to help you make the decision?
A) Pearson Product Moment correlation analysis
B) Independent Samples t tests
C) Cross-tabulation
D) Cross-tabulation with chi-square tests
E) Paired samples tabulation analysis

Accepted Answer

The best type of analysis to conduct in this scenario is Pearson Product Moment correlation analysis, as it will allow you to examine the relationship between the two variables ("intention to purchase" and "attitude toward the brand name") and determine if there is a significant positive correlation between them. This information can then be used to make a decision on the brand name for the new product.

Question 4

If you plotted data between two variables and the points all fell precisely in a straight line that was sloping downward to the right, your correlation coefficient would be equal to +1.0.

Accepted Answer

A correlation coefficient of -1.0 would indicate a perfect negative correlation, where all the points fall precisely in a straight line sloping downward to the right. A correlation coefficient of +1.0 would indicate a perfect positive correlation, where all the points fall precisely in a straight line sloping upward to the right.

Question 5

We would use the Pearson correlation table if we wanted to visualize the nonmonotonic relationship between two nominal scaled variables.

Accepted Answer

The Pearson correlation table is used to visualize the linear relationship between two continuous variables. It is not appropriate for nominal scaled variables, as they cannot be measured on a continuous scale. Additionally, the lack of a monotonic relationship means that there is no clear trend in the data, so a visualization tool like a scatterplot or contingency table may be more appropriate.

Question 6

The Pearson Product Moment correlation measures the linear relationship between two interval-scaled and/or ratio-scaled variables.

Accepted Answer

The statement accurately describes the use and measurement of Pearson Product Moment correlation.

Question 7

If you have a significant and very strong correlation, greater than .90, you may assume there is a causal relationship between the two variables.

Accepted Answer

Correlation does not necessarily imply causation. Even if there is a strong correlation between two variables, there may be other underlying factors or explanations that contribute to the relationship, and it is important to conduct further research and analysis before making any conclusions about causality.

Question 8

The Pearson Product Moment correlation measures the linear relationship between two nominal and/or ordinal scaled variables.

Accepted Answer

The Pearson Product Moment correlation measures the linear relationship between two continuous variables, not nominal or ordinal variables. For nominal and ordinal variables, other correlation measures such as Spearman's or Kendall's correlation should be used.

Question 9

The SPSS command used to execute the Pearson product moment correlation is: ANALYZE; CORRELATE; BIVARIATE.

Accepted Answer

This is the correct command to execute Pearson product moment correlation in SPSS.

Question 10

If you are examining the SPSS output for Pearson correlation, you will find the value of "1" along the diagonal of the correlation matrix indicating that each variable is perfectly correlated with itself.

Accepted Answer

The diagonal of the correlation matrix always displays the value of "1" because each variable is perfectly correlated with itself.

Question 11

Michelle Steward is a marketing professor at Wake Forest University. Michelle had been asked by the administration to study a sample of classes at Wake to help the university understand the student population better particularly in terms of factors that differentiate students with high versus low GPAs. One of the questions asked was: "What score did you earn (0 to 100)on the last test that you took?" and another question in the study asked "How much time, estimated in numbers of minutes, did you study for the last test you took?" Michelle decided to run a cross-tabulation analysis on these two questions. When she did, she also ran the chi-square test. The result was a Pearson Chi-Square Value of 8.64 and a p value reported as a "Sig." in SPSS of .03. Michelle knew that this meant:
A) there was a significant, positive association between the two variables
B) there was the presence of an association; the probability of supporting the null hypothesis that there is no association is only 3 percent
C) there was the presence of a negative association; the probability of supporting the null hypothesis that there is no association is only 3 percent
D) there was the presence of a positive, "very strong" association because the probability of supporting the null hypothesis that there is no association is only .03 percent
E) None of the above; Michelle should not have run a chi-square test because the two variables are both metric.

Accepted Answer

The answer of Michelle Steward is a marketing professor at...

Question 12

You are an officer in your college's Student Marketing Association. You are looking for ways to ensure that members will join again the following year. Students tend to join for one semester or one year and then drop out. You decide to take a simple random sample of this year's members and give them a survey. One of the questions asks: Will you join the SMA next semester? Yes, No, Don't Know. Another question asks respondents to check all the following that they feel provides them with "value" by virtue of being in the SMA: free food at meetings, getting to socialize in a relaxed setting with fellow classmates, learning about businesses through the guest speaker program, getting job search information through the organization's "Career Search" program, and getting to know your professors on a more personal basis. You want to know which one(s)of these are related to whether or not students will join the SMA in the next semester. What analysis should you run?
A) association analysis between the question that asks if they will join and each one of the remaining questions
B) differences analysis between the question that asks if they will join and each one of the remaining questions
C) predictive analysis between the question that asks if they will join and each one of the remaining questions
D) determinant analysis between the question that asks if they will join and each one of the remaining questions
E) None of the above; there is no analysis that will help identify which of these issues are related to rejoining the SMA.

Accepted Answer

Association analysis would be the best choice to determine which of the listed factors are related to whether or not students will join the SMA in the next semester. This type of analysis can reveal any patterns or trends between variables and their likelihood of occurring together. By running an association analysis between the question asking if they will rejoin and each of the listed factors, the organization can identify which factors are most strongly associated with membership retention and focus on improving those areas to retain more members.

Question 13

Michelle Steward is a marketing professor at Wake Forest University. Michelle had been asked by the administration to study a sample of classes at Wake to help the university understand the student population better, particularly in terms of factors that differentiate students with high versus low GPAs. One of the questions asked was: "Did you pass or fail the last test you took?" and another question in the study asked "Did you study or not study for the last test you took?" Michelle decided to run a cross-tabulation analysis on these two questions. When she did she also ran the chi-square test. The result was a Pearson Chi-Square Value of 8.64 and a p value reported as a "Sig." in SPSS of .03. Michelle knew that this meant:
A) there was a significant, positive association between the two variables
B) there was the presence of an association; the probability of supporting the null hypothesis that there is no association is only 3 percent
C) there was the presence of a negative association; the probability of supporting the null hypothesis that there is no association is only 3 percent
D) there was the presence of a positive, "very strong" association because the probability of supporting the null hypothesis that there is no association is only .03 percent
E) None of the above; Michelle should not have run a chi-square test because the two variables are both metric.

Accepted Answer

The Pearson Chi-Square Value and the p value suggest that there is the presence of an association between the two variables, and the probability of supporting the null hypothesis that there is no association is only 3 percent. This means that the association between passing/failing the last test and studying/not studying for it is statistically significant.

Question 14

You are an officer in your college's Student Marketing Association. You are looking for ways to ensure that members will join again the following year. Students tend to join for one semester or one year and then drop out. You decide to take a simple random sample of this year's members and give them a survey. One of the questions asks: Will you join the SMA next semester? Yes, No, Don't Know. Another question asks respondents to check all the following that they feel provides them with "value" by virtue of being in the SMA: free food at meetings, getting to socialize in a relaxed setting with fellow classmates, learning about businesses through the guest speaker program, getting job search information through the organization's "Career Search" program, and getting to know your professors on a more personal basis. You want to know which one(s)of these are related to whether or not students will join the SMA in the next semester. What analysis should you run?
A) Pearson Product Moment correlation analysis
B) Independent Samples t tests
C) Cross-tabulation
D) Cross-tabulation with chi-square tests
E) Paired samples tabulation analysis

Accepted Answer

Cross-tabulation with chi-square tests would be the best choice to analyze the relationship between the factors that provide value to the students and their intention to join the SMA again in the next semester. This is because the survey question regarding whether or not they will join again next semester is nominal/categorical data and the question about factors providing value is also categorical. Cross-tabulation with chi-square tests will allow us to see if there is any significant relationship between these two variables.

Quiz 18: Determining and Interpreting Associations Among Variables