Editing and Coding: Transforming Raw Data into Information

1. Editing and Coding: Transforming Raw Data into Information (Source: W.G Zikmund, B.J Babin, J.C Carr and M. Griffin, Business Research Methods, 8th Edition, U.S, South-Western Cengage Learning, 2008)

2. Objectives • Know when a response is really an error and should be edited • Appreciate coding of pure qualitative research • Understand the way data are represented in a data file • Understand the coding of structured responses including a dummy variable approach • Appreciate the ways that technological advances have simplified the coding process

3. Stages of Data Analysis • Raw Data • The unedited responses from a respondent exactly as indicated by that respondent. • Nonrespondent Error • Error that the respondent is not responsible for creating, such as when the interviewer marks a response incorrectly. • Data Integrity • The notion that the data file actually contains the information that the researcher is trying to obtain to adequately address research questions.

4. Editing • Editing • The process of checking the completeness, consistency, and legibility of data and making the data ready for coding and transfer to storage. • 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. • In-House Editing • A rigorous editing job performed by a centralized office staff.

5. Editing • Checking for Consistency • Respondents match defined population • Check for consistency within the data collection framework • Taking Action When Response is Obviously in Error • Change/correct responses only when there are multiple pieces of evidence for doing so. • Editing Technology • Computer routines can check for consistency automatically.

6. 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 process providing an educated guess for the missing response based on available information.

7. Editing for Completeness (cont’d) • What about missing data? • List-wise deletion • The entire record for a respondent that has left a response missing is excluded from use in statistical analysis. • Pair-wise deletion • Only the actual variables for a respondent that do not contain information are eliminated from use in statistical analysis.

8. Facilitating the Coding Process • 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. Coding Qualitative Responses • Coding • The process of assigning a numerical score or other character symbol to previously edited data. • Codes • Rules for interpreting, classifying, and recording data in the coding process. • The actual numerical or other character symbols assigned to raw data. • Dummy Coding • Numeric “1” or “0” coding where each number represents an alternate response such as “female” or “male.”

10. Coding Qualitative Data with Words

11. Data Storage Terminology in SPSS

12. A Data File Stored in SPSS

13. Computerized Survey Data Processing • Data Entry • The activity of transferring data from a research project to computers. • Optical Scanning System • A data processing input device that reads material directly from mark-sensed questionnaires.

14. Data View in SPSS Serves Much the Same Purpose of a Coding Sheet