Chapter Fifteen Chapter 15

OPENING QUESTIONS What is the nature and scope of data preparation, and how can the data preparation process be described? What is involved in questionnaire checking and editing? How should questionnaires be coded to prepare the data for analysis? What methods are available for cleaning the data and treating missing responses? Which ethical issues are important in data preparation and analysis?

Relationship to Marketing Figure 15.1 Relationship of Data Preparation to the Previous Chapters and the Marketing Research Process Focus of This Chapter Relationship to Previous Chapters Relationship to Marketing Research Process Preparing Data for Analysis Marketing Research Process (Chapter 1) Research Design Components (Chapter 3) Problem Definition Approach to Problem Figure 15.1 Relationship to the Previous Chapters & The Marketing Research Process Research Design Field Work Data Preparation and Analysis Report Preparation and Presentation

Figure 15.2 Data Preparation: An Overview Opening Vignette The Data Preparation Process Fig 15.3 Questionnaire Checking and Editing Fig 15.4 Coding What Would You Do? Be a DM! Be an MR! Experiential Learning Fig 15.5 Transcribing Fig 15.6 Data Cleaning Selecting Data Analysis Strategy Fig 15.7 Application to Contemporary Issues International Technology Ethics

Figure 15.3 Data Preparation Process Preliminary Plan of Data Analysis Figure 15.3 Data Preparation Process Questionnaire Checking Editing Coding Transcribing Data Cleaning Selecting a Data Analysis Strategy

Questionnaire Checking A questionnaire returned from the field may be unacceptable for several reasons. Parts of the questionnaire may be incomplete. The pattern of responses may indicate that the respondent did not understand or follow the instructions. The responses show little variance. One or more pages are missing. The questionnaire is received after the preestablished cutoff date. The questionnaire is answered by someone who does not qualify for participation.

Figure 15.4 Treatment of Unsatisfactory Responses Return to the Field Assign Missing Values Discard Unsatisfactory Respondents Substitute a Neutral Value Casewise Deletion Pairwise Deletion

Coding Guidelines for coding unstructured questions: Category codes should be mutually exclusive and collectively exhaustive. Only a few (10% or less) of the responses should fall into the “other” category. Category codes should be assigned for critical issues even if no one has mentioned them. Data should be coded to retain as much detail as possible.

Codebook A codebook contains coding instructions and the necessary information about variables in the data set. A codebook generally contains the following information: column number record number variable number variable name question number instructions for coding

Figure 15.5 A Codebook Excerpt Column Number 1-3 4 5-6 7-8 9-14 15-20 21-22 23-24 25 26 27 28 35 Variable Number 1 2 3 4 5 6 7 8 9 10 11 18 Variable Name Respondent ID Record Number Project Code Interview Code date Code Time Code Validation Code Blank Who shops Familiarity with store 1 Familiarity with store 2 Familiarity with store 3 Familiarity with store 10 Question Number I IIa IIb IIc IIj Coding Instructions 001 to 890 add leading zeros as necessary 1 (same for all respondents) 31 (same for all respondents) As coded on the questionnaire Leave these columns blank Male head =1 Female head =2 Other =3 Punch the number circled Missing values =9 For question II parts a through j Not so familiar =1 Very familiar =6 Missing Values =9

Figure 15.6 Data Transcription Raw Data Figure 15.6 Data Transcription CATI/ CAPI Key Punching via CRT Terminal Mark Sense Forms Optical Scanning Computerized Sensory Analysis Verification: Correct Key Punching Errors Computer Memory Disks Magnetic Tapes Transcribed Data

Figure 15.7 Selecting A Data Analysis Strategy Earlier Steps (1, 2, 3) of the Marketing Research Process Figure 15.7 Selecting A Data Analysis Strategy Known Characteristics of Data Properties of Statistical Techniques Background & Philosophy of the Researcher Data Analysis Strategy