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Chapter XIV

Learn about the data preparation process in marketing research, including questionnaire checking, editing, coding, transcribing, data cleaning, statistically adjusting the data, and selecting a data analysis strategy.

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Chapter XIV

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  1. Chapter XIV Data Preparation

  2. Chapter Outline 1) Overview 2) The Data Preparation Process 3) Questionnaire Checking 4) Editing i. Treatment of Unsatisfactory Responses 5) Coding i. Coding Questions ii. Code-book iii. Coding Questionnaires

  3. 6) Transcribing 7) Data Cleaning i. Consistency Checks ii. Treatment of Missing Responses 8) Statistically Adjusting the Data i. Weighting ii. Variable Respecification iii. Scale Transformation 9) Selecting a Data Analysis Strategy Adjusting the Data

  4. 10) A Classification of Statistical Techniques 11) Ethics in Marketing Research 12) Internet & Computer Applications 13) Focus on Burke 14) Summary 15) Key Terms and Concepts 16) Acronyms

  5. Data Preparation Process Fig. 14.1 Prepare Preliminary Plan of Data Analysis Check Questionnaire Edit Code Transcribe Clean Data Statistically Adjust the Data Select Data Analysis Strategy

  6. An Illustrative Computer File Table 14.1 Fields Column Numbers Records 1-3 4 5-6 7-8 ... 26 ... 35 77 Record 1 001 1 31 01 6544234553 5 Record 11 002 1 31 01 5564435433 4 Record 21 003 1 31 01 4655243324 4 Record 31 004 1 31 01 5463244645 6 Record 2701 271 1 31 55 6652354435 5

  7. Data Transcription Fig. 14.4 Raw Data Keypunching via CRT Terminal CATI/ CAPI Computerized Sensory Analysis Mark Sense Forms Optical Scanning Verification:Correct Keypunching Errors Disks Computer Magnetic Tapes Memory Transcribed Data

  8. Selecting a Data Analysis Strategy Fig. 14.5 Earlier Steps (1,2, & 3) of the Marketing Research Process Known Characteristics of the Data Properties of Statistical Techniques Background and Philosophy of the Researcher Data Analysis Strategy

  9. A Classification of Univariate Techniques Fig. 14.6 Univariate Techniques Non-numeric Data Metric Data Two or More Samples One Sample Two or More Samples One Sample • Frequency • Chi-Square • K-S • Runs • Binomial * t test * Z test Independent Related * Two- Groupt test * Z test * One-Way ANOVA Independent Related * Paired * t test * Chi-Square * Mann-Whitney * Median * K-S * K-W ANOVA * Sign * Wilcoxon * McNemar * Chi-Square

  10. A Classification of Multivariate Techniques Fig. 14.7 Multivariate Techniques Dependence Technique Interdependence Technique One Dependent Variable More Than One Dependent Variable Interobject Similarity Variable Interdependence * Cross- Tabulation * Analysis of Variance and Covariance * Multiple Regression * Conjoint Analysis * Multivariate Analysis of Variance and Covariance * Canonical Correlation * Multiple Discriminant Analysis * Factor Analysis * Cluster Analysis * Multidimensional Scaling

  11. Nielsen’s Internet Survey: “Does It Carry Any Weight?” RIP14.1 The Nielsen Media Research Company, a longtime player in television-related marketing research has come under fire from the various TV networks for its surveying techniques. Additionally, in another potentially large, new revenue business, Internet surveying, Nielsen is encountering serious questions concerning the validity of its survey results. Due to the tremendous impact of electronic commerce on the business world, advertisers need to know how many people are doing business on the Internet in order to decide if it would be lucrative to place their ads online. Nielsen performed a survey for CommerceNet, a group of companies that includes Sun Microsystems and American Express, to help determine the number of total users on the Internet.

  12. Nielsen’s research stated that 37 million people over the age of 16 have access to the Internet and 24 million have used the Net in the last three months. Where statisticians believe the numbers are flawed is in the weighting used to help match the sample to the population. Weighting must be used to prevent research from being skewed towards one demographic segment.

  13. The Nielsen survey was weighted for gender but not for education which may have skewed the population towards educated adults. Nielsen then proceeded to weight the survey by age and income after they had already weighted it for gender. Statisticians also feel that this is incorrect because weighting must occur simultaneously, not in separate calculations. Nielsen does not believe the concerns about their sample are legitimate and feel that they have not erred in weighting the survey. However, due to the fact that most third parties have not endorsed Nielsen’s methods, the validity of their research remains to be established.

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