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Understanding Data Preparation Techniques in Research for Accurate Results

This chapter focuses on the essential steps in data preparation, highlighting the importance of editing raw data to identify errors and omissions. It explains coding methods for categorizing responses, including handling open-ended questions through content analysis. Key issues such as 'don't know' responses and missing data are addressed, providing guidance on data entry, manipulation, and the use of tools like QSR’s XSight software. By mastering these techniques, researchers can ensure the integrity and accuracy of their data, paving the way for reliable analysis and conclusions.

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Understanding Data Preparation Techniques in Research for Accurate Results

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  1. Chapter 18 Data Preparation and Description

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

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

  4. Exhibit 18-1 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 18-2 Sample Codebook

  9. Exhibit 18-3 Precoding

  10. Exhibit 18-3 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. Open-Question Coding Example

  15. Handling Don’t Know Responses 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

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