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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. Data Preparation in the Research Process

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

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

  7. 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.

  8. 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

  9. Sample Codebook

  10. Precoding

  11. Coding Open-Ended Questions

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

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

  14. Content Analysis

  15. Types of Content Analysis Syntactical Referential Propositional Thematic

  16. Open-Question Coding

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

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

  19. Missing Data Listwise Deletion Pairwise Deletion Replacement

  20. 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

  21. Appendix 15a Describing Data Statistically

  22. Frequencies A B

  23. Distributions

  24. Characteristics of Distributions

  25. Measures of Central Tendency Mean Median Mode

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

  27. Summarizing Distribution Shape

  28. Symbols _ _ _

  29. 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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