Type
Quiz
Book Title
Basic Marketing Research: Using Microsoft Excel Data Analysis 3rd Edition
ISBN 13
978-0135078228

978-0135078228 Chapter 13 Lecture Note

June 7, 2019
CHAPTER 13
Finding Differences
LEARNING OBJECTIVES
To understand how market segmentation underlies differences analysis
To learn how to assess the significance of the difference between two groups’ percentages
To learn how to assess the significance of the difference between two groups’ averages
To understand when “analysis of variance” (ANOVA) is used and how to interpret ANOVA
findings
To comprehend what analysis is used when the averages of two variables using the same
metric scale are compared for differences
To gain knowledge of the “Differences” analyses available with XL Data Analyst
CHAPTER OUTLINE
Why Are Differences Important?
Testing for Significant Differences Between Two Groups
Differences Between Percentages for Two Groups
Using the XL Data Analyst to Determine the Significance of the Difference between Two
Group Percents
Differences Between Averages for Two Groups
Using the XL Data Analyst to Determine the Significance of the Difference Between Two
Group Averages
Testing for Significant Differences for More Than Two Group Averages
Why Use Analysis of Variance?
Using the XL Data Analyst to Determine the Significance of the Difference Among More
Than Two Group Averages
Flow Chart of Differences Analyses for Groups
Testing for Significant Differences Between the Averages of Two Variables
Using the XL Data Analyst to Determine the Significance of the Differences Between the
Averages of Two Variables
How to Present Difference Analysis Findings
KEY TERMS
Alternative hypothesis
Analysis of variance (ANOVA)
Group comparison table
Grouping variable
Market segmentation
Standard error of a difference
between two averages
Standard error of the difference
between two percentages
Target variable
TEACHING SUGGESTIONS
1. The chapter begins by claiming that market segmentation is very important and the basis for
market researchers to investigate statistically significant differences among identifiable
groups of consumers. Instructors may use their favorite or most familiar market segmentation
example to augment or emphasize the segmentation differences central point.
2. To identify market segments is only the beginning point of the marketing research notion of
significant differences. Once the segments are identified, marketing research requires that
data be gathered about the consumption patterns of the market segments, and then, these
patterns are assessed statistically for significant differences. The statistical concepts used to
compare segments are percentages and averages.
3. It is important to emphasize that marketing segmentation is a conceptual notion, whereas
segments can be identified in a great many ways they are not managerially relevant until
statistically significant differences between them are shown, that are useful from a marketing
strategy standpoint.
As a simple example, take a florist that segments the local market by geographic area: North,
East, West, and South. The average dollars spent per purchase is calculated for three
flower-giving days in the year. Assume that differences of ±$5 are not significant.
Area Father’s Day Mother’s Day Valentine’s Day
North $20 $17 $50
East $21 $31 $14
West $15 $17 $25
South $55 $36 $10
What are the promotional implications of these findings?
Answer by day.
Father’s Day—promote heavily to the South
Mother’s Day—promote heavily to the South and East
Valentine’s Day—promote heavily to the North, moderately to the West, and lightly to the
East and South
4. The differences between groups is taken with percentages first because the formulas are less
complicated, and students can relate to them easier than they can relate to the averages
differences formulas. The discussion moves quickly from formulas to XL Data Analyst
interpreted output where the differences are identified for users. Instructors who are more
concerned with their students’ learning the formulas may wish to spend more time on
formulas and have students do in-class or other exercises that require them to use these
formulas correctly.
5. The assumption of equal variances in the two samples of a test for the significance of the
difference between two group averages is not discussed in the text’s coverage of these
computations. This is because the XL Data Analyst tests this condition and uses the correct
formula based on this test. Instructors who desire their students to understand the equal vs.
unequal variances notion in this statistical test will need to draw on material from sources
other than this textbook.
6. The description of one-way analysis of variance is not in depth. Instructors who desire
students to have more knowledge of ANOVA may use this description as a foundation and
move to a more in-depth coverage with their own materials.
7. The Schaffer’s post hoc test was selected above other post hoc tests due to its popularity and
easy of computation. It is planned that “pro” version of the XL Data Analyst will detect
conditions such as variance differences and group sample size differences and select the most
appropriate post hoc test.
8. Due to space constraints, the “How to Present Difference Analysis Finding” does not include
any examples. Interested Instructors should consult our larger textbook, Marketing Research,
6th edition, by the same authors, for examples.
9. The integrated case, Advanced Automotive Concepts, in this chapter can be combined with
the case in the next chapter. With a large class, Instructors can divide the class up (perhaps
according to first letter of last name) and assign students the task of determining the target
market for one of the new model concepts (super cycle, runabout sport, etc.). The case in
Chapter 14 requires students to use the target market findings to determine promotional
vehicle recommendations.