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.