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Chapter 16

Chapter 16. Exploring, Displaying, and Examining Data. Understand . . . That exploratory data analysis techniques provide insights and data diagnostics by emphasizing visual representations of the data.

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Chapter 16

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  1. Chapter 16 Exploring, Displaying, and Examining Data

  2. 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. Learning Objectives

  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, partner • Zanthus

  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.

  5. Exploratory Data Analysis Exploratory Confirmatory

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

  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, author • Innovation and Entrepreneurship

  8. Frequency: Appropriate Social Networking Age

  9. Bar Chart

  10. Pie Chart

  11. Frequency Table

  12. Histogram

  13. Stem-and-Leaf Display 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 455666788889 12466799 02235678 02268 24 018 3 1 06 3 36 3 6 8

  14. Pareto Diagram

  15. Boxplot Components

  16. Diagnostics with Boxplots

  17. Boxplot Comparison

  18. Mapping

  19. SPSS Cross-Tabulation

  20. Percentages in Cross-Tabulation

  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

  22. Cross-Tabulation with Control and Nested Variables

  23. Automatic Interaction Detection (AID)

  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.

  25. 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

  26. Chapter 16 Additional Discussion opportunities

  27. Snapshot: Novation No standarded vocabulary across companies Serve variety of users Ad hoc analysis with sophisticated visualizations Big data with sophisticated analytical tool.

  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

  29. Snapshot: Empowering Excel

  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 next decades . . . .”

  31. Research Thought Leader “As data availability continues to increase, the importance of identifying/filtering and analyzing relevant data can be a powerful way to gain an information advantage over our competition.” Tom H.C. Anderson founder & managing partner Anderson Analytics, LLC

  32. PulsePoint: Research Revelation 65 The percent boost in company revenue created by best practices in data quality.

  33. Geograph: Digital Camera Ownership

  34. CloseUp: Working with Data Tables

  35. CloseUp: Original Data Table

  36. CloseUp: Arranged by SpendingMost to Least

  37. CloseUp: Arranged by Average Annual Purchases, Most to Least

  38. CloseUp: Arranged by Average Transaction, Most to Least

  39. CloseUp: Arranged by Estimated Average Transaction, Least to Most

  40. Chapter 16 Exploring, Displaying, and Examining Data

  41. Photo Attributions

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