1. Chapter 19 Editing and Coding: Transforming Raw Data into Information © 2010 South-Western/Cengage Learning. All rights reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. EIGHTH EDITION BUSINESS MARKET RESEARCH ZIKMUND BABIN CARR GRIFFIN

2. © 2010 South-Western/Cengage Learning. All rights reserved. May not be scanned, copied or duplicated, or posted to a publically accessible website, in whole or in part.19–2 LEARNING OUTCOMES 1.Know when a response is really an error and should be edited 2.Appreciate coding of pure qualitative research 3.Understand the way data are represented in a data file 4.Understand the coding of structured responses including a dummy variable approach 5.Appreciate the ways that technological advances have simplified the coding process After studying this chapter, you should be able to

3. © 2010 South-Western/Cengage Learning. All rights reserved. May not be scanned, copied or duplicated, or posted to a publically accessible website, in whole or in part.19–3 Stages of Data Analysis Raw DataRaw Data  The unedited responses from a respondent exactly as indicated by that respondent. Nonrespondent ErrorNonrespondent Error  Error that the respondent is not responsible for creating, such as when the interviewer marks a response incorrectly. Data IntegrityData Integrity  The notion that the data file actually contains the information that the researcher is trying to obtain to adequately address research questions.

4. © 2010 South-Western/Cengage Learning. All rights reserved. May not be scanned, copied or duplicated, or posted to a publically accessible website, in whole or in part.19–4 EXHIBIT 19.1 Overview of the Stages of Data Analysis

5. © 2010 South-Western/Cengage Learning. All rights reserved. May not be scanned, copied or duplicated, or posted to a publically accessible website, in whole or in part.19–5 Editing EditingEditing  The process of checking the completeness, consistency, and legibility of data and making the data ready for coding and transfer to storage. Field EditingField 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 EditingIn-House Editing  A rigorous editing job performed by a centralized office staff.

6. © 2010 South-Western/Cengage Learning. All rights reserved. May not be scanned, copied or duplicated, or posted to a publically accessible website, in whole or in part.19–6 Editing Checking for ConsistencyChecking for Consistency  Respondents match defined population  Check for consistency within the data collection framework Taking Action When Response is Obviously in ErrorTaking Action When Response is Obviously in Error  Change/correct responses only when there are multiple pieces of evidence for doing so. Editing TechnologyEditing Technology  Computer routines can check for consistency automatically.

7. © 2010 South-Western/Cengage Learning. All rights reserved. May not be scanned, copied or duplicated, or posted to a publically accessible website, in whole or in part.19–7 Editing for Completeness Item NonresponseItem 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.

8. © 2010 South-Western/Cengage Learning. All rights reserved. May not be scanned, copied or duplicated, or posted to a publically accessible website, in whole or in part.19–8 Editing for Completeness (cont’d) What about missing data?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.

9. © 2010 South-Western/Cengage Learning. All rights reserved. May not be scanned, copied or duplicated, or posted to a publically accessible website, in whole or in part.19–9 Facilitating the Coding Process Editing And Tabulating “Don’t Know” AnswersEditing 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)

10. © 2010 South-Western/Cengage Learning. All rights reserved. May not be scanned, copied or duplicated, or posted to a publically accessible website, in whole or in part.19–10 Editing (cont’d) Pitfalls of EditingPitfalls of Editing  Allowing subjectivity to enter into the editing process.  Data editors should be intelligent, experienced, and objective.  A systematic procedure for assessing the questionnaire should be developed by the research analyst so that the editor has clearly defined decision rules. Pretesting EditPretesting Edit  Editing during the pretest stage can prove very valuable for improving questionnaire format, identifying poor instructions or inappropriate question wording.

11. © 2010 South-Western/Cengage Learning. All rights reserved. May not be scanned, copied or duplicated, or posted to a publically accessible website, in whole or in part.19–11 Coding Qualitative Responses CodingCoding  The process of assigning a numerical score or other character symbol to previously edited data. CodesCodes  Rules for interpreting, classifying, and recording data in the coding process.  The actual numerical or other character symbols assigned to raw data. Dummy CodingDummy Coding  Numeric “1” or “0” coding where each number represents an alternate response such as “female” or “male.”  If k is the number of categories for a qualitative variable, k-1 dummy variables are needed.

12. © 2010 South-Western/Cengage Learning. All rights reserved. May not be scanned, copied or duplicated, or posted to a publically accessible website, in whole or in part.19–12 EXHIBIT 19.2 Coding Qualitative Data with Words

13. © 2010 South-Western/Cengage Learning. All rights reserved. May not be scanned, copied or duplicated, or posted to a publically accessible website, in whole or in part.19–13 Data File Terminology FieldField  A collection of characters that represents a single type of data—usually a variable. String CharactersString Characters  Computer terminology to represent formatting a variable using a series of alphabetic characters (nonnumeric characters) that may form a word. RecordRecord  A collection of related fields that represents the responses from one sampling unit.

14. © 2010 South-Western/Cengage Learning. All rights reserved. May not be scanned, copied or duplicated, or posted to a publically accessible website, in whole or in part.19–14 Data File Terminology (cont’d) Data FileData File  The way a data set is stored electronically in spreadsheet-like form in which the rows represent sampling units and the columns represent variables. Value LabelsValue Labels  Unique labels assigned to each possible numeric code for a response.

15. © 2010 South-Western/Cengage Learning. All rights reserved. May not be scanned, copied or duplicated, or posted to a publically accessible website, in whole or in part.19–15 EXHIBIT 19.3 Data Storage Terminology in SPSS

16. © 2010 South-Western/Cengage Learning. All rights reserved. May not be scanned, copied or duplicated, or posted to a publically accessible website, in whole or in part.19–16 EXHIBIT 19.4 A Data File Stored in SPSS

17. © 2010 South-Western/Cengage Learning. All rights reserved. May not be scanned, copied or duplicated, or posted to a publically accessible website, in whole or in part.19–17 Code Construction Two Basic Rules for Coding Categories:Two Basic Rules for Coding Categories: 1. They should be exhaustive, meaning that a coding category should exist for all possible responses. 2. They should be mutually exclusive and independent, meaning that there should be no overlap among the categories to ensure that a subject or response can be placed in only one category. Test TabulationTest Tabulation  Tallying of a small sample of the total number of replies to a particular question in order to construct coding categories.

18. © 2010 South-Western/Cengage Learning. All rights reserved. May not be scanned, copied or duplicated, or posted to a publically accessible website, in whole or in part.19–18 EXHIBIT 19.5 Precoding Fixed-Alternative Responses

19. © 2010 South-Western/Cengage Learning. All rights reserved. May not be scanned, copied or duplicated, or posted to a publically accessible website, in whole or in part.19–19 EXHIBIT 19.6 Precoded Format for Telephone Interview

20. © 2010 South-Western/Cengage Learning. All rights reserved. May not be scanned, copied or duplicated, or posted to a publically accessible website, in whole or in part.19–20 EXHIBIT 19.7 Coding Open-Ended Questions about Chili

21. © 2010 South-Western/Cengage Learning. All rights reserved. May not be scanned, copied or duplicated, or posted to a publically accessible website, in whole or in part.19–21 Devising the Coding Scheme A coding scheme should not be too elaborate.A coding scheme should not be too elaborate.  The coder’s task is only to summarize the data.  Categories should be sufficiently unambiguous that coders will not classify items in different ways. Code bookCode book  Identifies each variable in a study and gives the variable’s description, code name, and position in the data matrix.

22. © 2010 South-Western/Cengage Learning. All rights reserved. May not be scanned, copied or duplicated, or posted to a publically accessible website, in whole or in part.19–22 EXHIBIT 19.8 Open-Ended Responses to a Survey about the Honolulu Airport

23. © 2010 South-Western/Cengage Learning. All rights reserved. May not be scanned, copied or duplicated, or posted to a publically accessible website, in whole or in part.19–23 Computerized Survey Data Processing Data EntryData Entry  The activity of transferring data from a research project to computers. Optical Scanning SystemOptical Scanning System  A data processing input device that reads material directly from mark-sensed questionnaires.

24. © 2010 South-Western/Cengage Learning. All rights reserved. May not be scanned, copied or duplicated, or posted to a publically accessible website, in whole or in part.19–24 Data View in SPSS Serves Much the Same Purpose of a Coding Sheet