1. BUSINESS MARKET RESEARCH
ZIKMUND BABIN
CARR GRIFFIN
BUSINESS MARKET RESEARCH
EIGHTH EDITION

2. LEARNING OUTCOMES After studying this chapter, you should be able to
Know what descriptive statistics are and why they are used
Create and interpret simple tabulation tables
Understand how cross-tabulations can reveal relationships
Perform basic data transformations
List different computer software products designed for descriptive statistical analysis
Understand a researcher’s role in interpreting the data
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3. The Nature of Descriptive Analysis
The elementary transformation of raw data in a way that describes the basic characteristics such as central tendency, distribution, and variability.
Histogram
A graphical way of showing a frequency distribution in which the height of a bar corresponds to the observed frequency of the category.
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4. EXHIBIT 20.1 Levels of Scale Measurement and Suggested Descriptive Statistics
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5. Creating and Interpreting Tabulation
The orderly arrangement of data in a table or other summary format showing the number of responses to each response category.
Tallying is the term when the process is done by hand.
Frequency Table
A table showing the different ways respondents answered a question.
Sometimes called a marginal tabulation.
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6. Frequency Table Example
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7. Cross-Tabulation Cross-Tabulation Contingency Table Marginals
Addresses research questions involving relationships among multiple less-than interval variables.
Results in a combined frequency table displaying one variable in rows and another variable in columns.
Contingency Table
A data matrix that displays the frequency of some combination of responses to multiple variables.
Marginals
Row and column totals in a contingency table, which are shown in its margins.
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8. EXHIBIT 20.2 Cross-Tabulation Tables from a Survey Regarding AIG and Government Bailouts
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9. EXHIBIT 20.3 Different Ways of Depicting the Cross-Tabulation of Biological Sex and Target Patronage
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10. Cross-Tabulation (cont’d)
Percentage Cross-Tabulations
Statistical base – the number of respondents or observations (in a row or column) used as a basis for computing percentages.
Elaboration and Refinement
Elaboration analysis – an analysis of the basic cross-tabulation for each level of a variable not previously considered, such as subgroups of the sample.
Moderator variable – a third variable that changes the nature of a relationship between the original independent and dependent variables.
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11. EXHIBIT 20.4 Cross-Tabulation of Marital Status, Sex, and Responses to the Question “Do You Shop at Target?”
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12. Cross-Tabulation (cont’d)
How Many Cross-Tabulations?
Every possible response becomes a possible explanatory variable.
When hypotheses involve relationships among two categorical variables, cross-tabulations are the right tool for the job.
Quadrant Analysis
An extension of cross-tabulation in which responses to two rating-scale questions are plotted in four quadrants of a two-dimensional table.
Importance-performance analysis
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13. EXHIBIT 20.5 An Importance-Performance or Quadrant Analysis of Hotels
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14. Data Transformation Data Transformation
Process of changing the data from their original form to a format suitable for performing a data analysis addressing research objectives.
Bimodal
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15. Problems with Data Transformations
Median Split
Dividing a data set into two categories by placing respondents below the median in one category and respondents above the median in another.
The approach is best applied only when the data do indeed exhibit bimodal characteristics.
Inappropriate collapsing of continuous variables into categorical variables ignores the information contained within the untransformed values.
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16. EXHIBIT 20.6 Bimodal Distributions Are Consistent with Transformations into Categorical Values
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17. EXHIBIT 20.7 The Problem with Median Splits with Unimodal Data
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18. Index Numbers Index Numbers Price indexes
Scores or observations recalibrated to indicate how they relate to a base number.
Price indexes
Represent simple data transformations that allow researchers to track a variable’s value over time and compare a variable(s) with other variables.
Recalibration allows scores or observations to be related to a certain base period or base number.
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19. EXHIBIT 20.8 Hours of Television Usage per Week
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20. Calculating Rank Order
Ranking data can be summarized by performing a data transformation.
The transformation involves multiplying the frequency by the ranking score for each choice resulting in a new scale.
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21. EXHIBIT 20.9 Executive Rankings of Potential Conference Destinations
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22. EXHIBIT 20.10 Frequencies of Conference Destination Rankings
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23. EXHIBIT 20.11 Pie Charts Work Well with Tabulations and Cross-Tabulations
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24. Computer Programs for Analysis
Statistical Packages
Spreadsheets
Excel
Statistical software:
SAS
SPSS (Statistical Package for Social Sciences)
MINITAB
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25. EXHIBIT 20.12 SAS Computer Output of Descriptive Statistics
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26. EXHIBIT 20.13 Examples SPSS Output for Cross-Tabulation
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27. Computer Graphics and Computer Mapping
Box and Whisker Plots
Graphic representations of central tendencies, percentiles, variabilities, and the shapes of frequency distributions.
Interquartile Range
A measure of variability.
Outlier
A value that lies outside the normal range of the data.
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28. EXHIBIT 20.14 A 3-D Graph Showing Fast-Food Consumption Patterns around the U.S.
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29. EXHIBIT 20.15 Computer Drawn Box and Whisker Plot
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30. Interpretation Interpretation
The process of drawing inferences from the analysis results.
Inferences drawn from interpretations lead to managerial implications and decisions.
From a management perspective, the qualitative meaning of the data and their managerial implications are an important aspect of the interpretation.
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31. CASE EXHIBIT 20.2–1 Shifts in Brand Choice Before and After Showing of Downy-Q Quilt Commercial
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32. CASE EXHIBIT 20.2–2 Pre/Post Increment in Choice of Downy-Q
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33. CASE EXHIBIT 20.2–3 Adjective Checklist for Downy-Q Quilt Commercial
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34. CASE EXHIBIT 20.2–4 Product Attribute Checklist for Downy-Q
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