2OPENING QUESTIONSWhat 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?
3Relationship to Marketing Figure Relationship of Data Preparation to the Previous Chapters and the Marketing Research ProcessFocus of ThisChapterRelationship toPrevious ChaptersRelationship to MarketingResearch ProcessPreparing Data for AnalysisMarketing Research Process (Chapter 1)Research Design Components (Chapter 3)Problem DefinitionApproach to ProblemFigure Relationship to the Previous Chapters & The Marketing Research ProcessResearch DesignField WorkData Preparationand AnalysisReport Preparationand Presentation
4Figure 15.2 Data Preparation: An Overview Opening VignetteThe Data Preparation ProcessFig 15.3Questionnaire Checking and EditingFig 15.4CodingWhat Would You Do?Be a DM! Be an MR! Experiential LearningFig 15.5TranscribingFig 15.6Data CleaningSelecting Data Analysis StrategyFig 15.7Application to Contemporary IssuesInternationalTechnologyEthics
5Figure 15.3 Data Preparation Process Preliminary Plan of Data AnalysisFigure Data Preparation ProcessQuestionnaire CheckingEditingCodingTranscribingData CleaningSelecting a Data Analysis Strategy
6Questionnaire 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.
7Figure 15.4 Treatment of Unsatisfactory Responses Return to theFieldAssign MissingValuesDiscardUnsatisfactoryRespondentsSubstitute aNeutral ValueCasewiseDeletionPairwiseDeletion
8Coding 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.
9CodebookA codebook contains coding instructions and the necessary information about variables in the data set. A codebook generally contains the following information:column numberrecord numbervariable numbervariable namequestion numberinstructions for coding
10Figure 15.5 A Codebook Excerpt ColumnNumber1-345-67-89-1415-2021-2223-242526272835VariableNumber123456789101118VariableNameRespondent IDRecord NumberProject CodeInterview Codedate CodeTime CodeValidation CodeBlankWho shopsFamiliarity with store 1Familiarity with store 2Familiarity with store 3Familiarity with store 10QuestionNumberIIIaIIbIIcIIjCodingInstructions001 to 890 add leading zeros asnecessary1 (same for all respondents)31 (same for all respondents)As coded on the questionnaireLeave these columns blankMale head =1Female head =2Other =3Punch the number circledMissing values =9For question II parts a through jNot so familiar =1Very familiar =6Missing Values =9
11Figure 15.6 Data Transcription Raw DataFigure Data TranscriptionCATI/CAPIKey Punching viaCRT TerminalMark SenseFormsOpticalScanningComputerizedSensoryAnalysisVerification: CorrectKey Punching ErrorsComputerMemoryDisksMagneticTapesTranscribed Data
12Figure 15.7 Selecting A Data Analysis Strategy Earlier Steps (1, 2, 3) of the Marketing Research ProcessFigure Selecting A Data Analysis StrategyKnown Characteristics of DataProperties of Statistical TechniquesBackground & Philosophy of the ResearcherData Analysis Strategy