45) A ________ allows a researcher to visually inspect the plotted points and possibly to spot a
systematic nonlinear relationship.
A) line graph
B) stacked bar chart
C) multiple line graph
D) scatter plot
46) Correlation will not detect ________ between variables.
A) nonlinear relationships
B) linear relationships
C) significant relationships between variables
D) parallel significance
47) Associative analyses determine whether stable relationships exist between two variables.
48) A relationship is a consistent and systematic linkage between the levels for two scale
variables or between the labels for two ordinal variables.
49) There are five basic types of relationships between two variables: linear, curvilinear,
elliptical, monotonic, and nonmonotonic.
50) In a linear relationship, knowledge of the amount of one variable will automatically yield
knowledge of the amount of the other variable as a consequence of applying the linear or
straight-line formula that is known to exist between them.
51) A curvilinear relationship means a very smooth curve pattern. However, it does not clearly
describe the association.
52) A monotonic relationship means the researcher knows the general direction (increasing or
decreasing) of the relationship between two variables.
53) In a nonmonotonic relationship, only the general pattern of presence or absence is known.
54) Presence refers to the finding that a systematic relationship exists between the two variables
of interest in the population, but is not a statistical issue.
55) With associative analysis, the null hypothesis states there is no association (relationship)
present in the population and the appropriate statistical test is applied to test this hypothesis.
56) Strong associations are those in which there is a high probability that the two variables will
exhibit independent relationships, regardless of the type of relationship being analyzed.
57) There is an orderly procedure for determining the presence, direction, and strength of a
relationship,
58) Some associative analysis statistics, such as correlations, may not indicate the strength of the
relationship in a straightforward manner–that is, just by their absolute size.
59) The greater the absolute size of the correlation coefficient, the greater is the covariation
between the two variables, or the stronger is their relationship.
60) A correlational survey was repeated many, many times and computed the average for a
correlation that was not significant across all of these surveys, however, eventually the
researcher would get some statistical significance.
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61) Any correlation that is equal to or greater than the absolute value of .20 is typically
uninteresting to marketing researchers because it rarely identifies a meaningful association
between two variables.
62) Rules of thumb exist concerning the strength of a correlation based on its absolute size.
63) The correlation for a ball-shaped scatter diagram is zero because there is no discernable
linear relationship.
64) The Pearson product moment correlation measures the circular relationship between two
interval-and/or ratio-scaled variables (scale variables) such as those depicted conceptually by
Venn diagrams.
65) The correlation coefficient that can be computed between two variables is a measure of the
“separation” of the scatter points to the straight line.
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66) Pearson product moment correlation and other linear association correlation coefficients
indicate not only the degree of association but the direction as well.
67) In using the Pearson product moment correlation, negative correlation coefficients reveal that
the relationship is opposite: As one variable increases, the other variable increases.
68) In using the Pearson product moment correlation, positive correlation coefficients reveal that
the relationship is increasing: Larger quantities of one variable are associated with larger
quantities of another variable.
69) It is important to note that the angle or the slope of an ellipse has everything to do with the
size of the correlation coefficient. Everything hinges on the width of the ellipse.
70) With correlation analysis, each correlation will have a unique significance level.
71) Researchers should follow a procedure for reporting correlation findings with one of the
primary guidelines being that the researcher must test to determine that a significant correlation
has been found before reporting it.
72) In reporting correlation findings, researchers should keep in mind that they must adhere to
the marketing research industry standard on how to report statistically significant correlations to
clients.
73) Within the guidelines on the reporting of correlation findings, marketing researchers usually
have a “target” or a “focal” variable in mind, and they look at correlations of other variables of
interest with this target variable.
74) Cross-tabulation can only be used for two variables; each variable must have well-defined
labels.
75) A cross-tabulation table is sometimes referred to as an “r × c” (r-by-c) table.
76) The frequencies table (often referred to as the observed frequencies table) contains calculated
numbers determined from the completed tabulation.
77) A cross-classification table can have four types of numbers in each cell: frequency, raw
percentage, column percentage, and row percentage.
78) The column percentages table divides the raw frequencies by the column total raw
frequency.
79) Raw percentages are row frequencies divided by the grand total.
80) Chi-square analysis assesses the statistical significance of monotonic associations in cross–
tabulation tables.
81) The formal procedure for Chi-square analysis begins when the researcher formulates a
statistical null hypothesis that the two variables under investigation are not associated in the
population.
82) When dealing with Chi-square, it is always necessary for the researcher to state a hypothesis
in a formal sense.
83) During Chi-square analysis, observed frequencies are compared to expected frequencies,
which are defined as the theoretical frequencies that are derived from this hypothesis of no
association between the two variables.
84) The Chi-square test statistic summarizes how close to the expected frequencies the observed
cell frequencies are found to be.
85) The Chi-square distribution is skewed to the right and the rejection region is always at the
right-hand tail of the distribution.
86) Unlike other types of statistical distributions, the Chi-square distribution’s shape is
determined by the number of degrees of constriction.
87) A table of Chi-square values contains critical points that determine the break between the
acceptance and rejection regions at various levels of significance.
88) Chi-square analysis is best interpreted as a prerequisite to looking more closely at the two
variables to discern the nature of the association that exists between them.
89) To reveal the nonmonotonic relationships found to be significant in cross-tabulation tables,
researchers often turn to PowerPoint presentations, as pictures will show the relationships well.
90) Bar charts can be used to “see” a nonmonotonic relationship.
91) Association analysis (including the correlation coefficient) explicitly assumes a cause-and-
effect relationship, which is a condition of one variable bringing about the other variable.
92) A correlation coefficient merely investigates the presence, strength, and direction of a linear
relationship between two variables.
93) There are instances in which a marketing researcher wants to see if there is a relationship
between the responses to one question and the responses to another question in the same survey.
Identify the four types of possible relationships. Discuss how they are differentiated.
94) Depending on its type, a relationship is usually characterized in three ways: by its presence,
direction, and strength of association. Discuss these three ways of characterizing relationships
and their meaning relative to association.
95) There is an orderly procedure for determining the presence, direction, and strength of a
relationship. Outline and briefly discuss the six steps involved in the procedure for analyzing the
relationship between two variables.
96) Associations can be characterized by presence, direction, and strength, depending on the
scaling assumptions of the questions being compared. Discuss how these characteristics are
readily seen in correlation analysis.
97) What is the Pearson product moment correlation coefficient? How does the Pearson product
moment correlation coefficient indicate not only degree of association but also the direction?
98) Define and discuss Chi-square analysis and its use in cross-tabulation.
99) With respect to Chi-square analysis describe observed and expected frequencies. What is the
relationship between observed/expected frequencies and the Chi-square statistic?
100) What are some of the special considerations researchers must consider when performing
association analyses such as correlations and cross-tabulations?