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Marketing
Research
Aaker, Kumar, Day
Seventh Edition
Instructor’s Presentation
Slides
Chapter Sixteen
Fundamentals of Data
Analysis
Data Analysis
A set of methods and techniques used
to obtain information and insights from
data
Helps avoid erroneous judgements and
conclusions
Can constructively influence the
research objectives and the research
design
Marketing Research 7th Edition
© Aaker, Kumar, Day
Preparing the Data for
Analysis
Data editing
Coding
Statistically adjusting the data
Marketing Research 7th Edition
© Aaker, Kumar, Day
Preparing the Data for
Analysis (Contd.)
Data Editing
Identifies omissions, ambiguities, and errors
in responses
Conducted in the field by interviewer and field
supervisor and by the analyst prior to data
analysis
Marketing Research 7th Edition
© Aaker, Kumar, Day
Preparing the Data for
Analysis (Contd.)
Problems Identified With Data Editing
Interviewer Error
Omissions
Ambiguity
Inconsistencies
Lack of Cooperation
Ineligible Respondent
Marketing Research 7th Edition
© Aaker, Kumar, Day
Preparing the Data for
Analysis (Contd.)
Coding
Coding closed-ended questions involves
specifying how the responses are to be
entered
Open-ended questions are difficult to code

Lengthy list of possible responses is generated
Marketing Research 7th Edition
© Aaker, Kumar, Day
Preparing the Data for
Analysis (Contd.)
Statistically Adjusting the Data + Weighting
Each response is assigned a number according to
a pre-specified rule
Makes sample data more representative of target
population on specific characteristics
Modifies number of cases in the sample that
possess certain characteristics
Adjusts the sample so that greater importance is
attached to respondents with certain
characteristics
Marketing Research 7th Edition
© Aaker, Kumar, Day
Preparing the Data for
Analysis (Contd.)
Statistically Adjusting the Data + Variable Respecification
Existing data is modified to create new variables
Large number of variables collapsed into fewer variables
Creates variables that are consistent with study objectives
Dummy variables are used (binary, dichotomous,
instrumental, quantitative variables)
Use (d-1) dummy variables to specify (d) levels of
qualitative variable
Marketing Research 7th Edition
© Aaker, Kumar, Day
Preparing the Data for
Analysis (Contd.)
Statistically Adjusting
Transformation
the
Data
+
Scale
Scale values are manipulated to ensure
comparability with other scales
Standardization allows the researcher to compare
variables that have been measured using different
types of scales
Variables are forced to have a mean of zero and a
standard deviation of one
Can be done only on interval or ratio scaled data
Marketing Research 7th Edition
© Aaker, Kumar, Day
Simple Tabulation
Consists of counting the number of cases
that fall into various categories
Use of Simple Tabulation
Determine empirical distribution (frequency
distribution) of the variable in question
Calculate summary statistics, particularly
the mean or percentages
Aid in "data cleaning" aspects
Marketing Research 7th Edition
© Aaker, Kumar, Day
Frequency Distribution
Reports the number of responses that each
question received
Organizes data into classes or groups of values
Shows number of observations that fall into each
class
Can be illustrated simply as a number or as a
percentage or histogram
Response categories may be combined for many
questions
Should result in categories with worthwhile number
of respondents
Marketing Research 7th Edition
© Aaker, Kumar, Day
Descriptive Statistics
Statistics normally associated with a
frequency distribution to help summarize
information in the frequency table
Measures of central tendency mean, median
and mode
Measures of dispersion (range, standard
deviation, and coefficient of variation)
Measures of shape (skewness and kurtosis)
Marketing Research 7th Edition
© Aaker, Kumar, Day
Analysis for Various
Population Subgroups
Differences between means or
percentages of two subgroup responses
can provide insights
Difference between means is concerned
with the association between two
questions
Question upon which means are based
are intervally scaled
Marketing Research 7th Edition
© Aaker, Kumar, Day
Cross Tabulations
Statistical analysis technique to study the
relationships among and between variables
Sample is divided to learn how the dependent
variable varies from subgroup to subgroup
Frequency distribution for each subgroup is
compared to the frequency distribution for the
total sample
The two variables that are analyzed must be
nominally scaled
Marketing Research 7th Edition
© Aaker, Kumar, Day
Factors Influencing the
Choice of Statistical
Technique
Type of Data

Classification of data involves nominal, ordinal, interval
and ratio scales of measurement

Nominal scaling is restricted to the mode as the only
measure of central tendency

Both median and mode can be used for ordinal scale

Non-parametric tests can only be run on ordinal data

Mean, median and mode can all be used to measure
central tendency for interval and ratio scaled data
Marketing Research 7th Edition
© Aaker, Kumar, Day
Factors Influencing the
Choice of Statistical
Technique (Contd.)
Research Design

Dependency of observations

Number of observations per object

Number of groups being analyzed

Control exercised over variable of interest
Assumptions Underlying the Test Statistic

If assumptions on which a statistical test is based
are violated, the test will provide meaningless
results
Marketing Research 7th Edition
© Aaker, Kumar, Day
Overview of Statistical
Techniques
Univariate Techniques

Appropriate when there is a single measurement of
each of the 'n' sample objects or there are several
measurements of each of the `n' observations but each
variable is analyzed in isolation

Nonmetric - measured on nominal or ordinal scale

Metric-measured on interval or ratio scale

Determine whether single or multiple samples are
involved

For multiple samples, choice of statistical test depends
on whether the samples are independent or dependent
Marketing Research 7th Edition
© Aaker, Kumar, Day
Overview of Statistical
Techniques (Contd.)
Multivariate Techniques
A collection of procedures for analyzing
association between two or more sets of
measurements that have been made on
each object in one or more samples of
objects
Dependence or interdependence
techniques
Marketing Research 7th Edition
© Aaker, Kumar, Day
Overview of Statistical
Techniques (Contd.)
Multivariate Techniques (Contd.)
Dependence Techniques
One or more variables can be identified as
dependent variables and the remaining as
independent variables
Choice of dependence technique depends on
the number of dependent variables involved
in analysis
Marketing Research 7th Edition
© Aaker, Kumar, Day
Overview of Statistical
Techniques (Contd.)
Multivariate Techniques (Contd.)
Interdependence Techniques
Whole set of interdependent
relationships is examined
Further classified as having focus on
variable or objects
Marketing Research 7th Edition
© Aaker, Kumar, Day
Overview of Statistical
Techniques (Contd.)
Why Use Multivariate Analysis?
To group variables or people or objects
To improve the ability to predict
variables (such as usage)
To understand relationships between
variables (such as advertising and
sales)
Marketing Research 7th Edition
© Aaker, Kumar, Day