1. Exploring, Displaying, and Examining Data
Chapter 16
Exploring, Displaying, and Examining Data
McGraw-Hill/Irwin
Business Research Methods, 10e
Copyright © 2008 by The McGraw-Hill Companies, Inc. All Rights Reserved.

2. Learning Objectives Understand . . .
That exploratory data analysis techniques provide insights and data diagnostics by emphasizing visual representations of the data.
How cross-tabulation is used to examine relationships involving categorical variables, serves as a framework for later statistical testing, and makes an efficient tool for data visualization and later decision-making.

3. PulsePoint: Research Revelation67
The percent of college students who see nothing unethical about swapping or downloading digital copyrighted files (software, music, movies) without paying for them.

4. Research Values the Unexpected
“It is precisely because the unexpected jolts us out of our preconceived notions, our assumptions, our certainties, that it is such a fertile source of innovation.”
Peter Drucker, author
Innovation and Entrepreneurship

5. Researcher Skill Improves Data Discovery
DDW is a global player in research services. As this ad proclaims, you can “push data into a template and get the job done,” but you are unlikely to make discoveries using that process.

6. Exploratory Data Analysis
Confirmatory

7. Data Exploration, Examination, and Analysis in the Research Process

8. Frequency of Ad Recall
Value Label Value Frequency Percent Valid Cumulative Percent Percent

9. Bar Chart

10. Pie Chart

11. Frequency Table

12. Histogram

13. Stem-and-Leaf Display5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
02268 24
018 3 1 06 36 6 8

14. Pareto Diagram

15. Boxplot Components

16. Diagnostics with Boxplots

17. Boxplot Comparison

18. Mapping

19. Geograph: Digital Camera Ownership

20. SPSS Cross-Tabulation

21. Percentages in Cross-Tabulation

22. Guidelines for Using Percentages
Averaging percentages
Use of too large percentages
Using too small a base
Percentage decreases can never exceed 100%

23. Cross-Tabulation with Control and Nested Variables

24. Automatic Interaction Detection (AID)

25. Exploratory Data Analysis
This Booth Research Services ad suggests that the researcher’s role is to make sense of data displays.
Great data exploration and analysis delivers insight from data.

26. Key Terms Automatic interaction detection (AID) Boxplot Cell
Confirmatory data analysis
Contingency table
Control variable
Cross-tabulation
Exploratory data analysis (EDA)
Five-number summary
Frequency table
Histogram
Interquartile range (IQR)
Marginals
Nonresistant statistics
Outliers
Pareto diagram
Resistant statistics
Stem-and-leaf display

27. Working with Data Tables

28. Our grateful appreciation to eMarketer for the use of their table.
Original Data Table
Our grateful appreciation to eMarketer for the use of their table.

29. Arranged by Spending

30. Arranged by No. of Purchases

31. Arranged by Avg. Transaction, Highest

32. Arranged by Avg. Transaction, Lowest