Bài giảng Business Research Methods - Chapter 16: Exploring, Displaying, and Examining Data

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.

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Chapter 16Exploring, Displaying, and Examining DataMcGraw-Hill/IrwinCopyright © 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved. 16-*Learning ObjectivesUnderstand . . .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-*Research as Competitive Advantage“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 partnerAnderson Analytics, LLC16-*PulsePoint: Research Revelation65The percent boost in company revenue created by best practices in data quality.16-*Researcher Skill Improves Data DiscoveryDDW 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-*Exploratory Data AnalysisConfirmatoryExploratory16-*Data Exploration, Examination, and Analysis in the Research Process16-*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 Entrepreneurship16-*Frequency of Ad Recall Value Label Value Frequency Percent Valid Cumulative Percent Percent16-*Bar Chart16-*Pie Chart16-*Frequency Table16-*Histogram 16-*Stem-and-Leaf Display4556667888891246679902235678022682401831063363685678910111213141516171819202116-*Pareto Diagram16-*Boxplot Components16-*Diagnostics with Boxplots16-*Boxplot Comparison16-*Mapping16-*Geograph: Digital Camera Ownership16-*SPSS Cross-Tabulation16-*Percentages in Cross-Tabulation16-*Guidelines for Using PercentagesAveraging percentagesUse of too large percentagesUsing too small a basePercentage decreases can never exceed 100%16-*Cross-Tabulation with Control and Nested Variables16-*Automatic Interaction Detection (AID)16-*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-*Key TermsAutomatic interaction detection (AID)BoxplotCellConfirmatory data analysisContingency tableControl variableCross-tabulationExploratory data analysis (EDA)Five-number summaryFrequency tableHistogramInterquartile range (IQR)MarginalsNonresistant statisticsOutliersPareto diagramResistant statisticsStem-and-leaf displayWorking with Data TablesMcGraw-Hill/IrwinCopyright © 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved. 16-*Original Data TableOur grateful appreciation to eMarketer for the use of their table.16-*Arranged by Spending16-*Arranged by No. of Purchases16-*Arranged by Avg. Transaction, Highest16-*Arranged by Avg. Transaction, Lowest