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Lecture Business research methods (12/e): Chapter 16 - Donald R. Cooper, Pamela S. Schindler

Chapter 16 - Exploring, displaying, and examining data. After studying this chapter you will be able to 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. | Exploring, Displaying, and Examining Data Chapter 16 1 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. 2 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 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. Exploratory Data . | Exploring, Displaying, and Examining Data Chapter 16 1 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. 2 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 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. Exploratory Data Analysis Confirmatory Exploratory 5 Data Exploration, Examination, and Analysis in the Research Process 6 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 Frequency: Appropriate Social Networking Age 8 Bar Chart 9 Pie Chart 10 Frequency Table 11 Histogram 12 Stem-and-Leaf Display 455666788889 12466799 02235678 02268 24 018 3 1 06 3 36 3 6 8 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 13 Pareto Diagram 14 Boxplot Components 15 Diagnostics with Boxplots 16 Boxplot Comparison 17 Mapping 18 SPSS Cross-Tabulation 19 Percentages in Cross-Tabulation 20 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 21 Cross-Tabulation with Control and Nested Variables 22 .