Data Preparation

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Chapter XIV
Data Preparation
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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
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6) Transcribing 7) Data Cleaning i.
Consistency Checks ii. Treatment of Missing
Responses
Adjusting the Data
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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
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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
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Data Transcription
Fig. 14.4
Raw Data
Keypunching via CRT Terminal
CATI/ CAPI
Computerized Sensory Analysis
Mark Sense Forms
Optical Scanning
VerificationCorrect Keypunching Errors
Disks
Computer
Magnetic Tapes
Memory
Transcribed Data
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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
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A Classification of Univariate Techniques
Fig. 14.6
Univariate Techniques
Non-numeric Data
Metric Data
One Sample
Two or More Samples
One Sample
Two or More Samples

• 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
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A Classification of Multivariate Techniques
Fig. 14.7
Multivariate Techniques
Dependence Technique
Interdependence Technique
One Dependent Variable
More Than One Dependent Variable
Variable Interdependence
Interobject Similarity
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
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Nielsens 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.
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Nielsens 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.
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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
Nielsens methods, the validity of their research
remains to be established.