Lecture Slides.pdf - Lecture 7 Data Preparation and Data Analysis Dr Junzhao Ma Department of Marke2ng MKF2121 Marketing Research Methods Todays agenda

Lecture Slides.pdf - Lecture 7 Data Preparation and Data...

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MKF21211Lecture 7 Data Preparation and Data Analysis Department of Marke2ngDr. Junzhao MaMKF2121 Marketing Research Methods Today’s agenda 2Data preparation – Coding – Cleaning – Re-coding Understanding your data – basic data characteristics Frequency (one way tabulation) Measures of location Measures of variability Logic of hypothesis testing Marketing Research Process Step 1: Defining the Problem Step 2: Developing an Approach to the Problem Step 3: Formulating a Research Design Step 4: Doing Field Work or Collecting Data Step 5: Preparing and Analyzing Data Step 6: Preparing and Presenting the Report 3
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MKF21212Today’s agenda 4Data preparation – Coding – Cleaning – Re-coding Understanding your data – basic data characteristics Frequency (one-way tabulation) Measures of location Measures of variability Logic of hypothesis testing Coding Preliminary Plan of Data Analysis Questionnaire Checking Editing Data Cleaning Data Transformation Selecting a Data Analysis Strategy 5Data Preparation Process 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 pre-established cutoff date. The questionnaire is answered by someone who does not qualify for participation. 6
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MKF21213Editing Editing involves reviewing questionnaires to increase accuracy and precision. The goal is to identify responses which are: – illegible – incomplete – inconsistent – ambiguous 7Treatment ofUnsa4sfactoryResponsesReturn to theFieldDiscardUnsa2sfactoryRespondentsAssign MissingValues8Editing Codingmeans assigning a code, usually a number, to each possible response to each question. 9Example - "Which grocery store is your favorite one in Melbourne?". Survey AnswerNumerical ValueWoolworth1Coles27-­‐Eleven3Aldi4COSTCO5Other6Missing (no response)9
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MKF21214Variable Number±Variable Name±Question Number (as in questionnaire)±Coding Instructions±1±ID±1 to 20 as coded±2±Preference±1±1=Weak Preference 7=Strong Preference±3±Quality±2±1=Poor 7=Excellent±4±Quantity±3±1=Poor 7=Excellent±5±Value±4±1=Poor 7=Excellent±6±Service±5±1=Poor 7=Excellent±7±Income±6±1 = Less than $20,000 ±2 = $20,000 to 34,999 ±3 = $35,000 to 49,999 ±4 = $50,000 to 74,999 ±5 = $75,000 to 99,999 ±6 = $100,00 or more ±Example: Codebook 11Restaurant Preference: SPSS VariableView 12Restaurant Preference: SPSS DataView
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MKF2121513Coding unstructured questions Example – “why do you buy from the bakery shop in the Caulfield Plaza? Name the onemost important reason”Define the categories and assign a code for each 1- Taste 2 - Convenience 3 - Price/discount 4 – Salmon sandwich
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