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.
If Maxwell House Coffee was considering a line of gourmet iced coffee, it would want
to know how coffee drinkers feel about gourmet iced coffee; that is, their attitudes
toward buying, preparing, and drinking it would be the dependent variables. Maxwell
House might consider developing:
A) a general conceptual model.