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Chapter XIV
Data Preparation
and Basic Data
Analysis
Important Topics of this Chapter
The Data Preparation Process
Questionnaire Checking
Editing
Coding
i. Coding Questionnaires
Data Cleaning
i. Consistency Checks
ii. Treatment of Missing Responses
Selecting a Data Analysis Strategy:
Descriptive Analysis
Inferential Analysis
Differential Analysis
Associative Analysis
Predictive Analysis
Adjusting
the Data
A Classification of Statistical Techniques
Understanding data Via Descriptive Statistics
Measure of Central Tendency
Mode
Median
Mean
Measure of Variability
Frequency Distribution
Range
Standard Deviation
Other Descriptive Measures
Measure of Skewness
Kurtosis
Obtaining Descriptive Statistics With SPSS
Fig. 14.1
Data Preparation Process
Prepare Preliminary Plan of Data Analysis
Check Questionnaire
Edit
Code
Transcribe
Clean Data
Statistically Adjust the Data
Select Data Analysis Strategy
Data Reduction
_
Summarization:
– Condensing the raw data into a few meaningful
computation.
_
Conceptualization:
– Visualization of what of these measures represent.
_
Communication:
– Translation of statistical analysis results into a form that
is understandable and, more important, useful to
marketing manager.
_
Interpolation:
– Assessment of data to the population
Types of Statistical Analysis
Used in Marketing Research
_
Descriptive Analysis:
– Mean, Mode, Median and Standard deviation.
_
Inferential Analysis:
– Hypothesis testing and estimation of true population values.
_
Differences Analysis:
– Determination of significant differences exit in the population.
_
Associative Analysis:
– Investigation of how two and more variables are related.
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Predictive Analysis:
– It is used to enhance prediction capabilities of marketing
researcher. Ex: regression analysis
Understanding Data Via
Descriptive Analysis
_
Measure of Central Tendency:
– Mode
» Highest occurrence in a set of variables.
– Median
» Occurrence in the middle of a set values.
– Mean:
» Arithmetic average of a set of numbers.
Understanding Data Via
Descriptive Analysis (cont.)
_
Measure of Variability:
– Frequency Distribution:
» Number of times that each different value appears.
– Range:
» Identifies the distance between the lowest and the highest value
in an ordered set of variables.
– Standard Deviation:
» The degree of variation or diversity in the values in a such a
way to be translated in a normal bell-shaped distribution.
Understanding the Data Via
Descriptive Statistics (cont.)
_
Other Descriptive Measures:
– Measure of Skewness:
» Reveals the degree of direction of asymmetry in a distribution.
A ‘0’ value indicates symmetric distribution, a negative value
indicates distribution has tail to the left, a positive value
indicates distribution has tail to the right.
– Kurtosis:
» How pointed and peaked a distribution appears. A ‘0’ value
indicates distribution is bell shaped, a negative value indicates
distribution is more flat, a positive value indicated distribution
is more peaked than the bell shaped curve.
Fig. 14.5
Selecting a Data Analysis Strategy
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
Fig. 14.6
A Classification of Univariate Techniques
Univariate Techniques
Non-numeric
Data
Metric Data
One Sample
* t test
* Z test
Two or More
Samples
Independent
* TwoGroups t
test
* Z test
* One-Way
ANOVA
Related
* Paired
* t test
One Sample
* Frequency
*Chi-Square
*K-S
*Runs
* Binomial
Two or More
Samples
Independent
* Chi-Square
* Mann-Whitney
* Median
* K-S
* K-W ANOVA
Related
* Sign
* Wilcoxon
* McNemar
* Chi-Square
A Classification of Multivariate Techniques
Fig. 14.7
Multivariate Techniques
Dependence
Technique
One Dependent
Variable
* CrossTabulation
* Analysis of
Variance and
Covariance
* Multiple
Regression
* Conjoint
Analysis
More Than One
Dependent
Variable
* Multivariate
Analysis of
Variance and
Covariance
* Canonical
Correlation
* Multiple
Discriminant
Analysis
Interdependence
Technique
Variable
Interdependence
* Factor
Analysis
Interobject
Similarity
* Cluster Analysis
* Multidimensional
Scaling
RIP14.1
Nielsen’s Internet Survey:
“Does It Carry Any Weight?”
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.