Chapter XIV

Data Preparation

Chapter OutlineChapter Outline

1) Overview1) Overview

2) The Data Preparation Process2) The Data Preparation Process

3) Questionnaire Checking3) Questionnaire Checking

4) Editing4) Editing

i. Treatment of Unsatisfactory Responsesi. Treatment of Unsatisfactory Responses

5) Coding5) Coding

i. Coding Questionsi. Coding Questions

ii. Code-bookii. Code-book

iii. Coding Questionnairesiii. Coding Questionnaires

6) Transcribing6) Transcribing

7) Data Cleaning7) Data Cleaning

i. Consistency Checksi. Consistency Checks

ii. Treatment of Missing Responsesii. Treatment of Missing Responses

8) Statistically Adjusting the Data8) Statistically Adjusting the Data

i. Weightingi. Weighting

ii. Variable Respecificationii. Variable Respecification

iii. Scale Transformationiii. Scale Transformation

9) Selecting a Data Analysis Strategy9) Selecting a Data Analysis Strategy

Adjusting the Data

10) A Classification of Statistical Techniques10) A Classification of Statistical Techniques

11) Ethics in Marketing Research11) Ethics in Marketing Research

12) Internet & Computer Applications12) Internet & Computer Applications

13) Focus on Burke13) Focus on Burke

14) Summary14) Summary

15) Key Terms and Concepts15) Key Terms and Concepts

16) Acronyms16) Acronyms

Prepare Preliminary Plan of Data Analysis

Data Preparation ProcessData Preparation ProcessFig. 14.1Fig. 14.1

Check Questionnaire

Edit

Code

Select Data Analysis Strategy

Transcribe

Statistically Adjust the Data

Clean Data

RecordsRecords 1-31-3 44 5-65-6 7-8 ... 26 ...7-8 ... 26 ... 3535 7777

Record 1 001Record 1 001 11 3131 0101 6544234553 6544234553 5 5 Record 11 002Record 11 002 11 3131 0101 5564435433 5564435433 4 4Record 21 003Record 21 003 11 3131 0101 4655243324 4655243324 4 4Record 31 004Record 31 004 11 3131 0101 5463244645 5463244645 6 6Record 2701 271Record 2701 271 11 3131 5555 6652354435 6652354435 5 5

FieldsFieldsColumn NumbersColumn Numbers

An Illustrative Computer FileAn Illustrative Computer FileTable 14.1Table 14.1

Raw Data

Keypunching via CRT Terminal

Optical Scanning

Mark Sense Forms

Magnetic Tapes

Data TranscriptionData TranscriptionFig. 14.4Fig. 14.4

Computerized Sensory Analysis

CATI/ CAPI

Transcribed Data

Computer Memory Disks

Verification:Correct Keypunching Errors

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

Selecting a Data Analysis StrategySelecting a Data Analysis StrategyFig. 14.5Fig. 14.5

Univariate Techniques

Metric Data

Independent

A Classification of Univariate TechniquesA Classification of Univariate TechniquesFig. 14.6Fig. 14.6

Non-numeric Data

One Sample Two or More Samples

One Sample Two or More Samples

Related

Independent Related

* t test * Z test

* Frequency*Chi-Square*K-S*Runs* Binomial

* Two- Groupt test

* Z test * One-Way

ANOVA

* Paired* t test

* Chi-Square* Mann-Whitney* Median* K-S* K-W ANOVA

* Sign* Wilcoxon* McNemar* Chi-Square

Multivariate Techniques

Dependence Technique

A Classification of Multivariate TechniquesA Classification of Multivariate TechniquesFig. 14.7Fig. 14.7

More Than One Dependent Variable

* Multivariate Analysis of Variance and Covariance

* Canonical Correlation

* Multiple Discriminant Analysis

* Cross- Tabulation

* Analysis of Variance and Covariance

* Multiple Regression

* Conjoint Analysis

* Factor Analysis

Interdependence Technique

One Dependent Variable

Variable Interdependence

Interobject Similarity

* Cluster Analysis* Multidimensional

Scaling

Nielsen’s Internet Survey: Nielsen’s Internet Survey:

““Does It Carry Any Weight?”Does It Carry Any Weight?”

RIP14.1RIP14.1

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.

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. .

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..