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Chapter 16

Chapter 16. Data Preparation and Description. Learning Objectives. Understand . . . 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 16

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  1. Chapter 16 Data Preparation and Description

  2. Learning Objectives Understand . . . • 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 • use of content analysis to interpret and summarize open questions

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

  4. Exhibit 16-1 Data Preparation in the Research Process

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

  6. Field Editing • Field editing review • Entry gaps identified • Callbacks made • Validate results

  7. 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

  8. Exhibit 16-2 Sample Codebook

  9. Exhibit 16-3 Precoding

  10. Exhibit 16-3 Coding Open-Ended Questions

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

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

  13. Types of Content Analysis Syntactical Referential Propositional Thematic

  14. Exhibit 16-4 & 16-5Open-Question Coding

  15. Exhbit 16-7 Handling “Don’t Know” Responses Question: Do you have a productive relationship with your present salesperson?

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

  17. Missing Data Listwise Deletion Pairwise Deletion Replacement

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

  19. Appendix 16a Describing Data Statistically

  20. Frequencies A B

  21. Distributions

  22. Characteristics of Distributions

  23. Measures of Central Tendency Mean Median Mode

  24. Variance Quartile deviation Standard deviation Interquartile range Range Measures of Variability Dispersion

  25. Summarizing Distributions with Shape

  26. Symbols _ _ _

  27. 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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