CHAPTER 11
Summarizing Your Data
LEARNING OBJECTIVES
To see an overview of the four types of data analysis performed by market researchers
To understand summarization data analysis, including when to use percentage, average,
mode, and standard deviation
To become acquainted with the summarize function of your XL Data Analyst
To learn how to create effective presentation tables for categorical and metric variable
summarizations
CHAPTER OUTLINE
Types of Data Analysis Used in Marketing Research
Summarizing the Sample
Generalizing the Findings
Finding Meaningful Differences
Identifying Relationships
Summarizing Your Sample Findings
Summarizing Categorical Variables
How to Summarize Categorical Variables with XL Data Analyst
Summarizing Metric Variables
How to Summarize Metric Variables with XL Data Analyst
Flow Chart of Summarization Analysis
KEY TERMS
Average
Central tendency
Data analysis
Dataset
Differences analysis
Frequency distribution
Generalization analysis
Mode
Percentage distribution
Range
Relationship analysis
Standard Deviation
Summarization analysis
Variability
TEACHING SUGGESTIONS
1. Chapter 11 describes descriptive analysis in detail. The four other types of analysis—
summarization, generalization, differences analysis, and relationships analysis—are
described very briefly simply to provide an overview. Each type is described in detail in the
following chapters. For Chapter 11, students only need to know the definitions and basic
purpose of these analysis types. Table 11.1 is a concise summary of the various types of
analysis. Instructors may want to refer to this table and Figure 11.1 which compares various
types of data analysis with respect to complexity and value as a preview of what topics will
be covered in coming classes.
2. Some instructors may consider the descriptions of the measures of central tendency and
variability too elementary. Students with a good statistics course in their backgrounds,
theoretically, should know these concepts. However, many students seem to forget or repress
these concepts, and especially, standard deviation. The descriptions and the computational
examples are intended to help students recover their knowledge of these concepts. Virtually,
no students are required to compute descriptive statistics by hand, and the examples are
useful in helping them to “see” how the computations take place.
3. Measures of skewness and kurtosis are mentioned but not described in detail as marketing
researchers rarely use them, and managers do not understand them. Instructors who want
their students to understand these concepts can provide supplementary reading from statistics
texts or other sources. The current version of the XL Data Analyst does not report these
measures.
4. It is vital that students understand the relationship between a variable’s type of scale and the
appropriate descriptive analysis measure. They are summarized in Table 11.2.It is
recommended that instructors quiz students, or otherwise devote class time to ensuring that
students retain this knowledge that categorical variables should be summarized with
frequencies and percentage distributions while metric variables should be summarized with
averages, standard deviations, and the range.
5. The use of descriptive statistics can be illustrated effectively by using a research report.
Display the questionnaire used in the study, and have students identify the scaling
assumptions and appropriate descriptive statistics for various questions. Then show the tables
in the report that communicate the findings.
6. Instructors who want a different data set (such as a team project dataset) or who want to have
students learn first-hand how to build an XL Data Analyst data set should remind students
that the procedure for inputting their own data into the XL Data Analyst is in the previous
chapter in the section titled, “Data Analyst Case Datasets and Building your own XL Data
Analyst Dataset.” It gives step-by-step instructions for importing a CSV dataset and setting
up the variable descriptions, value codes, and value labels.