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Chapter 18. Data Preparation and Description. 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
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Chapter 18 Data Preparation and Description
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
Learning Objectives • Understand the problems and solutions for “don’t know” responses and handling missing data • Understand the options for data entry and manipulation
Accurate Consistent Uniformly entered Arranged for simplification Complete Editing Criteria
Field Editing • Field editing review • Entry gaps identified • Callbacks made • Validate results
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
Coding Rules Exhaustive Appropriate to the research problem Categories should be Mutually exclusive Derived from one classification principle
Content Analysis QSR’s XSight software for content analysis.
Types of Content Analysis Syntactical Referential Propositional Thematic
Handling Don’t Know Responses Do you have a productive relationship with your present salesperson?
Keyboarding Database Programs Optical Recognition Digital/ Barcodes Voice recognition Data Entry
Missing Data Listwise Deletion Pairwise Deletion Replacement
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