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Learning Objectives

Learning Objectives

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Learning Objectives

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  1. King Fahd University of Petroleum & MineralsDepartment of Management and MarketingMKT 345 Marketing ResearchDr. Alhassan G. Abdul-MuhminEditing and Coding Reference: Zikmund, Chapter 19

  2. Learning Objectives • At the end of this discussion you should be able to: • Explain the concepts of editing and coding • List the important considerations in editing and coding • List and explain the key issues in error-checking and data transformation • Explain the contents and uses of a code book • Edit and code completed questionnaires

  3. Overview of the Stages of Data Analysis

  4. EDITING The process of checking and adjusting responses in the completed questionnaires for omissions, legibility, and consistency and readying them for coding and storage

  5. Types of Editing 1. Field Editing • Preliminary editing by a field supervisor on the same day as the interview to catch technical omissions, check legibility of handwriting, and clarify responses that are logically or conceptually inconsistent. 2. In-house Editing • Editing performed by a central office staff; often dome more rigorously than field editing

  6. Purpose of Editing • For consistency between and among responses • For completeness in responses– to reduce effects of item non-response • To better utilize questions answered out of order • To facilitate the coding process

  7. Editing for Completeness • Item Nonresponse • The technical term for an unanswered question on an otherwise complete questionnaire resulting in missing data. • Plug Value • An answer that an editor “plugs in” to replace blanks or missing values so as to permit data analysis; choice of value is based on a predetermined decision rule. • Impute • To fill in a missing data point through the use of a statistical algorithm that provides a best guess for the missing response based on available information.

  8. Facilitating the Coding Process • Data Clean-up • Checking written responses for any stray marks • Editing And Tabulating “Don’t Know” Answers • Legitimate don’t know (no opinion) • Reluctant don’t know (refusal to answer) • Confused don’t know (does not understand)

  9. Editing (cont’d) • Pitfalls of Editing • Allowing subjectivity to enter into the editing process. • Data editors should be intelligent, experienced, and objective. • Failing to have a systematic procedure for assessing the questionnaires developed by the research analyst • An editor should have clearly defined decision rules to follow. • Pretesting Edit • Editing during the pretest stage can prove very valuable for improving questionnaire format, identifying poor instructions or inappropriate question wording.

  10. CODING • The process of identifying and classifying each answer with a numerical score or other character symbol • The numerical score or symbol is called a code, and serves as a rule for interpreting, classifying, and recording data •  Identifying responses with codes is necessary if data is to be processed by computer

  11. Coding - Continued • Coded data is often stored electronically in the form of a data matrix - a rectangular arrangement of the data into rows (representing cases) and columns (representing variables)The data matrix is organized into fields, records, and files: • Field: A collection of characters that represents a single type of data • Record: A collection of related fields, i.e., fields related to the same case (or respondent) • File: A collection of related records, i.e. records related to the same sample

  12. Key Issues in Coding • Pre-Coding Fixed-Alternative Questions (FAQs)-Writing codes for FAQs on the questionnaire before the data collection • Coding Open-Ended Questions - A 3-stage process: (a) Perform a test tabulation, (b) Devise a coding scheme, (c) Code all responses Two Rules For Code Construction are: • Coding categories should be exhaustive • Coding categories should be mutually exclusive and independent

  13. Issues in Coding - Continued • Maintaining a Code Book -A book that identifies each variable in a study, the variable’s description, code name, and position in the data matrix • Production Coding - The physical activity of transferring the data from the questionnaire or data collection form [to the computer] after the data has been collected. Sometimes done through a coding sheet – ruled paper drawn to mimic the data matrix • Combining Editing and Coding

  14. AFTER CODING ….. • Data Entry - The transfer of codes from questionnaires (or coding sheets) to a computer. Often accomplished in one of three ways: • On-line direct data entry – e.g. as for CATI systems • Optical scanning – for highly structured questionnaires • Keyboarding – data entry via a computer keyboard; often requires verification

  15. After Coding - Continued • Error Checking – Verifying the accuracy of data entry and checking for some kinds of obvious errors made during the data entry. Often accomplished through frequency analysis.

  16. After Coding - Continued • Data Transformation – Converting some of the data from the format in which they were entered to a format most suitable for particular statistical analysis. Often accomplished through re-coding, to: • reverse-score negative (or positive) statements into positive (or negative) statements; • collapse the number of categories of a variable