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Chapter 15

Chapter 15. Data Preparation and Description. Learning Objectives. Understand . . . The importance of editing the collected raw data to detect errors and omissions. How coding is used to assign number and other symbols to answers and to categorize responses.

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Chapter 15

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  1. Chapter 15 Data Preparation and Description

  2. Learning Objectives Understand . . . • The importance of editing the collected raw data to detect errors and omissions. • How coding is used to assign number and other symbols to answers and to categorize responses. • The use of content analysis to interpret and summarize open questions.

  3. Learning Objectives Understand . . . • Problems with and solutions for “don’t know” responses and handling missing data. • The options for data entry and manipulation.

  4. PulsePoint: Research Revelation 68 The percent of online consumers who put trust in online consumer recommendations.

  5. “In the future, we’ll stop moaning about the lack of perfect data and start using the good data with much more advanced analytics and data-matching techniques.” Kate Lynch, research director Leo Burnett’s Starcom Media Unit Research Adjusts for Imperfect Data

  6. Data Preparation in the Research Process

  7. Monitoring Online Survey Data Online surveys need special editing attention. CfMC provides software and support to research suppliers to prevent interruptions from damaging data .

  8. Accurate Consistent Uniformly entered Arranged for simplification Complete Editing Criteria

  9. Field Editing • Field editing review • Entry gaps identified • Callbacks made • Validate results Ad message: Speed without accuracy won’t help the manager choose the right direction.

  10. Central Editing Be familiar with instructions given to interviewers and coders Do not destroy the original entry Make all editing entries identifiable and in standardized form Initial all answers changed or supplied Place initials and date of editing on each instrument completed

  11. Sample Codebook

  12. Precoding

  13. Coding Open-Ended Questions

  14. Coding Rules Exhaustive Appropriate to the research problem Categories should be Mutually exclusive Derived from one classification principle

  15. Content Analysis QSR’s XSight software for content analysis.

  16. Content Analysis

  17. Types of Content Analysis Syntactical Referential Propositional Thematic

  18. Open-Question Coding

  19. Handling “Don’t Know” Responses Question: Do you have a productive relationship with your present salesperson?

  20. Keyboarding Database Programs Optical Recognition Digital/ Barcodes Voice recognition Data Entry

  21. Missing Data Listwise Deletion Pairwise Deletion Replacement

  22. Bar code Codebook Coding Content analysis Data entry Data field Data file Data preparation Data record Database Don’t know response Editing Missing data Optical character recognition Optical mark recognition Precoding Spreadsheet Voice recognition Key Terms

  23. Appendix 15a Describing Data Statistically

  24. Frequencies A B

  25. Distributions

  26. Characteristics of Distributions

  27. Measures of Central Tendency Mean Median Mode

  28. Variance Quartile deviation Standard deviation Interquartile range Range Measures of Variability

  29. Summarizing Distribution Shape

  30. Symbols _ _ _

  31. Central tendency Descriptive statistics Deviation scores Frequency distribution Interquartile range (IQR) Kurtosis Median Mode Normal distribution Quartile deviation (Q) Skewness Standard deviation Standard normal distribution Standard score (Z score) Variability Variance Key Terms

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