McGraw-Hill/IrwinCopyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved....
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McGraw-Hill/Irwin Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. EXPLORING, DISPLAYING, AND EXAMINING DATA Chapter 16
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Transcript of McGraw-Hill/IrwinCopyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved....
McGraw-Hill/Irwin Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved.
EXPLORING, DISPLAYING, AND EXAMINING DATA
Chapter 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 Display
455666788889
12466799
02235678
02268
24
018
3
1
06
3
36
3
6
8
5
6
7
8
9
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21
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 percentagesDon’t average percentages
Don’t use too large a percentage
Don’t use too large a percentage
Don’t use too small a baseDon’t use too small a base
Changes should never exceed 100%
Changes should never exceed 100%
Higher number is the denominator
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 OPPORTUNITIES
Chapter 16
16-27
Snapshot: Novation
No standarded vocabulary across companies
No standarded vocabulary across companies
Serve variety of usersServe variety of users
Ad hoc analysis with sophisticated visualizations
Ad hoc analysis with sophisticated visualizations
Big data with sophisticated analytical tool.
Big data with sophisticated analytical tool.
16-28
Snapshot: Digital Natives vs. Digital Immigrants
30 subjects = 15 natives, 15 immigrants
30 subjects = 15 natives, 15 immigrants
Monitored media behaviorsMonitored media behaviors
300 hours of real-time data300 hours of real-time data
Biometric Monitoring: emotional engagementBiometric Monitoring:
emotional engagement
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 DATA
Chapter 16
16-41
Photo Attributions
Slide Source
4 Courtesy of Radius Global Market Research
18 Courtesy of RealtyTrac
21 Vstock/Alamy
24 Courtesy of Booth Research Services
27 Courtesy of Novation
28 Realistic Reflections
29 Courtesy of DecisionPro; Digital Vision/Getty Images
30 Vstock LLC/Getty Images
