Chapter 14 study set

Which of the following correlation coefficients would indicate a "moderate" association? A) .85 B) -.85 C) -.75 D) .35 E) .60

-.75

A correlation coefficient is an index number constrained to fall between the range of: A) 0 and 1.00. B) 0 and 100. C) -1.00 and +1.00. D) -1.00 and 0. E) -100 and 0.

-1.00 and +1.00.

Assume that a researcher and client determine that a p value of .05 or less determines significance. Listed below are several correlation coefficients and their respective significance levels. Which correlation coefficient demonstrates an association not likely due to chance; that is, is it significant? A) .22, .06 B) .75, .05 C) .32, .15 D) .76, .95 E) .05, 1.00

.75, .05

If you wished to compute a Pearson product moment correlation coefficient using SPSS, which command sequence would you use? A) CORRELATE; PEARSON; GO B) ANALYZE; CORRELATE; PEARSON C) ANALYZE; CORRELATE; PEARSON; R D) ANALYZE; CORRELATE; PEARSON; GO E) ANALYZE; CORRELATE; BIVARIATE

ANALYZE; CORRELATE; BIVARIATE

In order to run a chi-square test using SPSS, the proper command sequence is: A) ANALYZE; CHI-SQUARE; GO. B) ANALYZE; SUMMARIZE; CROSSTABS; CHI-SQUARE. C) ASSOCIATIONS; NONMONO; CHI-SQUARE. D) ANALYZE; DESCRIPTIVE STATISTICS; CROSSTABS. E) ASSOCIATIONS; STATISTICS; CROSSTABS.

ANALYZE; DESCRIPTIVE STATISTICS; CROSSTABS.

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.

None of the above; Michelle should not have run a chi-square test because the two variables are both metric.

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

Pearson product moment correlation analysis

The intersection of a row and column in a cross-tabulation table is called: A) a cross-tabulation cell. B) a dangerous intersection. C) a chi-square. D) a cross-cell interaction. E) a row box.

a cross-tabulation cell.

Strength of association might be described as: A) strong. B) weak. C) moderate. D) all of the above E) none of the above

all of the above

Which of the following are caveats of correlation? A) Its use is limited to metric variables (interval or ratio scaled). B) It examines the relationship between only two variables. C) Do not assume cause and effect. D) It is limited to linear relationships. E) all of the above

all of the above

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 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.

association analysis between the question that asks if they will join and each one of the remaining questions

What type of analysis would be used to answer a question such as "Which customer demographic is most strongly related to product purchase/nonpurchase"? A) association or relationship analysis B) predictive analysis C) predictive or relationship analysis D) analysis of variance E) canine/feline regression analysis

association or relationship analysis

Pontiac wants to know what types of persons respond favorably to proposed style changes in the Firebird. Frito-Lay wants to know what kinds of people buy from the Frito-Lay line. These are questions that may be answered through: A) relationship analysis. B) chi-square analysis. C) associative analysis. D) analysis of variance. E) regression analysis.

associative analysis.

When a consistent and systematic relationship is found between two variables, the linkage is: A) causal. B) statistical. C) managerially significant. D) important. E) relevant.

causal.

You do not have a "relationship" that links the labels (or levels) for two variables unless the relationship is: A) causal and consistent. B) consistent and systematic. C) systematic and causal. D) systematic and important. E) consistent and important.

consistent and systematic.

What is used to determine whether a nonmonotonic relationship exists between two nominal-scaled variables? A) tabulation analysis and t tests B) cross-tabulation and chi-square tests C) cross-tabulation and t tests D) tabulation analysis and chi-square tests E) only t tests

cross-tabulation and chi-square tests

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 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

cross-tabulation with chi-square tests

The product life cycle curve that describes the sales pattern of a new product over time (growing slowly during its introduction and then spurting upward rapidly during its growth stage and finally plateauing or slowing down considerably as the market becomes saturated) is an example of which type of relationship? A) causal relationship B) linear relationship C) nonmonotonic relationship D) monotonic relationship E) curvilinear relationship

curvilinear relationship

Which type of relationship is described by relationships that may be S-shaped or J-shaped? A) causal relationship B) linear relationship C) curvilinear relationship D) nonmonotonic relationship E) irregular relationship

curvilinear relationship

In chi-square analysis, the greater the number of cells, the larger the degrees of freedom. The greater the number of cells, the more opportunity exists to calculate a large chi-square value. In other words, the chi-square value can be "inflated" not due to a real association but simply due to the fact that there are more cells in the analysis. This is why degrees of freedom are used to: A) determine how many cells you should analyze. B) determine whether or not the computed chi-square value should be used for a post hoc test. C) determine whether or not the chi-square value has a probability high enough to support, or not support, the null hypothesis. D) all of the above E) None of the above; degrees of freedom is not calculated with the chi-square test.

determine whether or not the chi-square value has a probability high enough to support, or not support, the null hypothesis.

If you were to find a significant association between two nominally scaled variables, a good way to present the findings in your cross-tabulation table would be to use: A) a p value for each nonmonotonic relationship found. B) a Sig. value for each nonmonotonic relationship found. C) graphical presentations. D) numerical presentations that clearly indicate the direction and strength of the relationship. E) numerical presentations that clearly indicate the linear values in the relationship.

graphical presentations.

The chi-square test is useful for determining: A) if a nonmonotonic relationship exists between two nominal-scaled variables. B) if a monotonic relationship exists between two nominal-scaled variables. C) if a nonmonotonic relationship exists between two interval-scaled variables. D) if a duotonic relationship exists between two variables. E) if a duotonic relationship exists between three variables.

if a nonmonotonic relationship exists between two nominal-scaled variables.

The Pearson product moment correlation measures the linear relationship between two: A) nominal- or ordinal-scaled variables. B) nominal- or interval-scaled variables. C) interval- or ratio-scaled variables. D) nominal- or ratio-scaled variables. E) ordinal- or interval-scaled variables.

interval- or ratio-scaled variables.

If we were to graph two variables, let's say, height (in inches) and GPA, and the graph showed points scattered about in a formless shape, we could say there is: A) likely no significant relationship between height and GPA. B) likely a positive relationship between height and GPA. C) likely a negative relationship between height and GPA. D) a need to re-graph the data. E) likely a curvilinear relationship between height and GPA.

likely no significant relationship between height and GPA.

Which type of relationship is a "straight-line" relationship between two variables and for which allows us to know the knowledge of one variable if we have knowledge of the other? A) causal relationship B) linear relationship C) monotonic relationship D) nonmonotonic relationship E) curvilinear relationship

linear relationship

Which type of relationship is described by the formula: y = a + bx? A) causal relationship B) linear relationship C) monotonic relationship D) nonmonotonic relationship E) curvilinear relationship

linear relationship

The owner of a shoe store knows that as children increase in age, their shoe size tends to get larger. This is an example of what type of relationship? A) causal relationship B) duotonic relationship C) monotonic relationship D) nonmonotonic relationship E) linear relationship

monotonic relationship

Which type of relationship would you have when you have a general direction assigned to the relationship; that is, as one variable increases, the other variable may increase (or decrease)? A) causal relationship B) duotonic relationship C) monotonic relationship D) nonmonotonic relationship E) curvilinear relationship

monotonic relationship

When a scale has "labels" as opposed to "levels," we can normally assume the level of measurement is: A) nominal or categorical. B) ratio. C) interval or ordinal. D) interval or ratio. E) metric.

nominal or categorical.

In the textbook you were given an example of running a chi-square test using SPSS. The output shows a "Pearson Chi-Square" value of 23.272 and df =7. This information alone means: A) there is a significant difference. B) there is no significant difference. C) the difference is associative. D) the test was run incorrectly. E) none of the above

none of the above

In the textbook you were given an example of running a chi-square test using SPSS. The output shows a "Pearson Chi-Square" value of 23.272. This value alone means: A) there is a significant difference. B) there is no significant difference. C) the difference is associative. D) the difference must immediately be rounded up. E) none of the above

none of the above

We know that McDonald's customers drink coffee for breakfast and soft drinks at lunch. This is an example of what type of relationship? A) causal relationship B) linear relationship C) monotonic relationship D) nonmonotonic relationship E) curvilinear relationship

nonmonotonic relationship

A relationship in which the presence (or absence) of one variable is systematically associated with the presence (or absence) of another is: A) causal relationship. B) linear relationship. C) monotonic relationship. D) nonmonotonic relationship. E) alinear relationship.

nonmonotonic relationship.

The four basic types of relationships between two variables are: A) a non-monotonic, duotonic, linear, and curvilinear. B) nonmonotonic, duotonic, sublinear, and curvilinear. C) nonmonotonic, monotonic, linear, and curvilinear. D) causal, consistent, systematic, and linear. E) duotonic, linear, sublinear, and alinear.

nonmonotonic, monotonic, linear, and curvilinear.

Which of the following is NOT a number that can be found in each cross-classification table cell? A) frequency B) raw percentage C) column percentage D) overall percentage E) row percentage

overall percentage

Which of the following is used when describing the general pattern of nonmonotonic relationships? A) presence B) direction C) strength D) pattern E) concreteness

pattern

Which of the following refers to the finding that a systematic relationship exists between the two variables of interest in the population? A) presence B) direction C) strength D) pattern E) concreteness

presence

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: A) nonpresence of a regressive relationship. B) presence (label) or amount of another variable. C) presence (label) or amount of the same variable. D) covariance of the other variable. E) presence of unobservable variables.

presence (label) or amount of another variable.

Which of the following were discussed by the authors as being ways that we may characterize relationships? A) presence, direction, concreteness B) direction, strength, concreteness C) presence, direction, strength D) presence, strength, concreteness E) presence, pattern, concreteness

presence, direction, strength

A cross-tabulation table is sometimes referred to as a: A) tabular table. B) nonmonotonic display table. C) r × c table. D) t × n table. E) c × x table.

r × c table.

A researcher runs a correlation analysis between two variables that she is certain are associated but the analysis indicates the two variables are not associated. The researcher may then want to: A) run another association test and add three variables. B) adopt a lower standard for determining significance, that is, a p value of .20. C) do nothing; if the association is deemed insignificant it is inappropriate to run further analyses. D) run a scatter plot in search of a curvilinear relationship. E) run a scatter plot in search of a linear relationship.

run a scatter plot in search of a curvilinear relationship.

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: A) smoking tends to increase. B) smoking tends to decrease. C) smoking changes 89 percent. D) smoking is nonexistent. E) only 89 out of every 100 people in the study would not smoke.

smoking tends to decrease.

If we run the chi-square test and we get a .02 level of significance to support the null hypothesis, this means: A) there is adequate support for the null hypothesis. B) there is no association between the two nominally scaled variables. C) there is only a 2 percent chance that the two nominally scaled variables are systematically related. D) there is a significant association between the two nominally scaled variables, and this information alone is adequate to explain the nature of the association. E) there is a significant association between the two nominally scaled variables, but this information alone is a "flag" that we need to look more closely at the variables to discern the nature of the relationship.

there is a significant association between the two nominally scaled variables, but this information alone is a "flag" that we need to look more closely at the variables to discern the nature of the relationship.

In the textbook you were given an example of running a chi-square test using SPSS. The output shows a "Pearson Chi-Square" value of 23.272, df = 7 and the Asymp. Sig. = .002. This means: A) there is a significant association. B) there is no significant association. C) the difference is associative. D) the test was run incorrectly. E) none of the above

there is a significant association.

The manager of the city's professional hockey team conducted a large survey. He wanted to know if there was an association between fans being "season ticket holders" versus "nonseason ticket holders" and whether they "bought" versus "didn't buy" hockey team merchandise at the game. Because his survey included these measurements, he used SPSS to run a Pearson product moment correlation coefficient that turned out to be .88 with a Sig. value of .001. This meant that: A) there is no significant relationship. B) there is a significant, strong, positive relationship. C) there is a significant, strong, negative relationship. D) there is a significant, moderate, positive relationship. E) None of the above; Pearson's product moment correlation is not the appropriate measure of association here because both variables are nominally scaled.

there is a significant, moderate, positive 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: A) there is 94.1 percent probability for supporting the null hypothesis. B) there is little or no probability for supporting the null hypothesis. C) there is little or no probability for supporting the null hypothesis, and there is a strong association. D) there is little or no probability for supporting the null hypothesis, and there is a strong, positive association. E) that more information is needed.

there is little or no probability for supporting the null hypothesis, and there is a strong, positive association.

The logic of the chi-square test would argue that, for a significant relationship to exist: A) there should be large differences between the observed and expected frequencies. B) there should be few differences between the observed and expected frequencies. C) there should be no differences between the observed and expected frequencies. D) there should be negative differences between the observed and expected frequencies. E) there should be only one difference between the observed and expected frequencies.

there should be large differences between the observed and expected frequencies.

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).

there was the presence (aka "significant") of a positive, "very strong" association between the variables.

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.

there was the presence of an association; the probability of supporting the null hypothesis that there is no association is only 3 percent

Associative analyses determine whether stable relationships exist between: A) costs and expenses. B) two variables. C) 12 or more variables. D) marketing and sales. E) statistics and results.

two variables.