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Data Editing and Cleaning for Effective Analysis

This chapter focuses on the inspection, correction, and handling of incomplete, wrong, and lackadaisical data answers. Learn about examples, coding techniques, and strategies to deal with missing data to optimize your analysis.

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Data Editing and Cleaning for Effective Analysis

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  1. Data Preparation for Analysis Chapter 11

  2. Editing • “The inspection and correction of the data received from each element of the sample.”

  3. Editing • Incomplete Answers • What to do? • General rule • Wrong Answers • Dog food example • Bookstore example • Answers that Reflect Lack of Interest • Examples

  4. Coding • “Transforming raw data into symbols” • Closed-ended items • Genders • Scales

  5. Coding • Open-ended items • Factual • Examples • Exploratory open-ended • Use more than one coder • Go through questionnaire and highlight responses • Specify the categories in which responses are to be placed • Code responses into categories • Compare results between coders

  6. Coding • Building the data file • The Codebook • Variable name • How coded • How to treat missing data

  7. Cleaning the Data • Blunders • An error that arises during editing, coding, or data entry • How to find • Checking • Double-entry • Optical scanning

  8. Handling Missing Data • Item Nonresponse • What to do? • Eliminate entire case • Eliminate case from analysis with that variable • Substitute missing values • Contact the respondent

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