Exam 18: Determining and Interpreting Associations Among Variables

Associative analysis procedures are useful because they determine if there is a consistent and systematic relationship between the presence (label)or amount of one variable and the:

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?

Let's assume we find in a study that the correlation coefficient between number of years of education and cigarette smoking is -.89. This means that as education level increases:

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:

There is no connection between scatter diagrams and correlation coefficients.

Which of the following is NOT a number that can be found in each cross-classification table cell?

A correlation coefficient is an index number constrained to fall between the range of:

If we run the chi-square test and we get a .02 level of significance to support the null hypothesis, this means:

In the chi-square analysis, the greater the differences between the observed frequencies and the expected frequencies, the less likely it is that there will be a statistically significant relationship.

Let's assume we use SPSS to run a correlation analysis, and we get a Pearson correlation coefficient of .941 and a Sig. value of .000. These figures mean:

To run chi-square analysis in SPSS, you should go to the Crosstabs command, click Statistics, then select Chi-Square.

Which type of relationship is described by the formula: y = a + bx?

A correlation coefficient is an index number constrained to fall between the range of -1.0 and +1.0 that communicates both the strength and the direction of association between three or more variables.

Coffee orders at a restaurant are present at breakfast and soft drink orders are present at lunch. This is an example of a monotonic relationship.

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.

Associative analysis procedures identify if there are consistent and systematic relationships between variables.

The fact that most tourists at sunny beach resorts use sun block is a curvilinear relationship.

A relationship in which the presence (or absence)of one variable is systematically associated with the presence (or absence)of another is:

The intersection of a row and column in a cross-tabulation table is called:

The key to establishing a monotonic relationship between two variables is determining if there is a general direction (increasing or decreasing)between the two variables.