Chapter 16

McGraw-Hill/Irwin Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved.

EXPLORING, DISPLAYING, AND EXAMINING DATAChapter 16

16-2

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.

16-3

Pull Quote

“On a day-to-day basis, look for inspiration and ideas outside the research industry to influence your thinking. For example, data visualization could be inspired by an infographic you see in a favorite magazine, or even a piece of art you see in a museum.”

Amanda Durkee, partnerZanthus

16-4

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 a template process.

16-5

Exploratory Data Analysis

ConfirmatoryExploratory

16-6

Data Exploration, Examination, and Analysis in the Research Process

16-7

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, authorInnovation and Entrepreneurship

16-8

Frequency: Appropriate Social Networking Age

16-9

Bar Chart

16-10

Pie Chart

16-11

Frequency Table

16-12

Histogram

16-13

Stem-and-Leaf Display455666788889124667990223567802268

240183106336

3

68

56789

101112131415161718192021

16-14

Pareto Diagram

16-15

Boxplot Components

16-16

Diagnostics with Boxplots

16-17

Boxplot Comparison

16-18

Mapping

16-19

SPSS Cross-Tabulation

16-20

Percentages in Cross-Tabulation

16-21

Guidelines for Using Percentages

Don’t average percentages

Don’t use too large a percentage

Don’t use too small a base

Changes should never exceed 100%

Higher number is the denominator

16-22

Cross-Tabulation with Control and Nested Variables

16-23

Automatic Interaction Detection (AID)

16-24

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.

16-25

Key Terms

Automatic interaction detection (AID)

BoxplotCellConfirmatory data

analysisContingency tableControl variableCross-tabulationExploratory data

analysis (EDA)

Five-number summary

Frequency tableHistogramInterquartile range

(IQR)MarginalsNonresistant

statisticsOutliersPareto diagramResistant statisticsStem-and-leaf

display

McGraw-Hill/Irwin Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved.

ADDITIONAL DISCUSSION OPPORTUNITIESChapter 16

16-27

Snapshot: Novation

No standarded vocabulary across companies

Serve variety of users

Ad hoc analysis with sophisticated visualizations

Big data with sophisticated analytical tool.

16-28

Snapshot: Digital Natives vs. Digital Immigrants

30 subjects = 15 natives, 15 immigrants

Monitored media behaviors

300 hours of real-time data

Biometric Monitoring: emotional engagement

16-29

Snapshot: Empowering Excel

16-30

Snapshot: Internet-age Researchers

“The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the nextdecades . . . .”

16-31

Research Thought Leader

“As data availability continues to increase, theimportance of identifying/filtering and analyzingrelevant data can be a powerful way to gain aninformation advantage over our competition.”

Tom H.C. Anderson founder & managing partner

Anderson Analytics, LLC

16-32

PulsePoint: Research Revelation

65 The percent boost in company revenue created by best practices in data quality.

16-33

Geograph: Digital Camera Ownership

16-34

CloseUp: Working with Data Tables

16-35

CloseUp: Original Data Table

16-36

CloseUp: Arranged by SpendingMost to Least

16-37

CloseUp: Arranged by Average Annual Purchases, Most to Least

16-38

CloseUp: Arranged by Average Transaction, Most to Least

16-39

CloseUp: Arranged by Estimated Average Transaction, Least to Most

McGraw-Hill/Irwin Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved.

EXPLORING, DISPLAYING, AND EXAMINING DATAChapter 16

16-41

Photo AttributionsSlide Source

4 Courtesy of Radius Global Market Research18 Courtesy of RealtyTrac21 Vstock/Alamy24 Courtesy of Booth Research Services27 Courtesy of Novation

28 Realistic Reflections29 Courtesy of DecisionPro; Digital Vision/Getty

Images30 Vstock LLC/Getty Images