Lecture Marketing research (12th edition) - Chapter 16: Fundamentals of data analysis
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Marketing Research
Aaker, Kumar,
Leone and Day
Twelfth Edition
Instructor’s
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Chapter Sixteen
Fundamentals of Data
Analysis
Marketing Research 12th Edition
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Data Analysis
•
A set of methods and techniques used to obtain information and
insights from data
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Helps avoid erroneous judgments and conclusions
•
Can constructively influence the research objectives and the research
design
Major Data Preparation techniques:
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Data editing
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Coding
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Statistically adjusting the data
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Data Editing
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Identifies omissions, ambiguities, and errors in
responses
Conducted in the field by interviewer and field
supervisor and by the analyst prior to data
analysis
Problems identified with data editing:
▫ Interviewer Error
▫ Omissions
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Coding
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Coding closedended questions involves specifying
how the responses are to be entered
Openended questions are difficult to code
▫ Lengthy list of possible responses is generated
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Statistically Adjusting the Data
Weighting
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Each response is assigned a number according to a prespecified 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
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Statistically Adjusting the Data (Contd.)
Variable Respecification
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Existing data are modified to create new variables
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Large number of variables are collapsed into fewer variables
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Creates variables that are consistent with study objectives
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Dummy variables are used
▫
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Binary, dichotomous, instrumental, quantitative variables)
Use (d1) dummy variables to specify (d) levels of qualitative variable
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Statistically Adjusting the Data (Contd.)
Scale Transformation
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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 ratioscaled data
Standardized score,
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Simple Tabulation
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Consists of counting the number of cases that fall
into various categories
Uses:
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Determine empirical distribution (frequency distribution) of
the variable in question
•
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Calculate summary statistics, particularly the mean or
percentages
Aid in "data cleaning" aspects
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Frequency Distribution
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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
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Frequency Distribution
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Descriptive Statistics
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Statistics normally associated with a frequency distribution
to help summarize information in the frequency table
Includes:
▫ Measures of central tendency mean, median and mode
▫ Measures of dispersion (range, standard deviation, and
coefficient of variation)
▫ Measures of shape (skewness and kurtosis)
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Cross Tabulations
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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
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Factors Influencing the Choice of Statistical
Technique
Types of Data
▫
Classification of data involves nominal, ordinal, interval and ratio
scales of measurement
▫
Nominal scaling is restricted in that mode is the only meaningful
measure of central tendency
▫
Both median and mode can be used for ordinal scale
▫
Nonparametric 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
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Factors Influencing the Choice of
Statistical Technique (Contd.)
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Research Design
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Depends on:
▫
Whether dependent or independent samples are used
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Number of observations per object
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Number of groups being analyzed
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Number of variables
▫
Control exercised over variable of interest
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Factors Influencing the Choice of
Statistical Technique (Contd.)
Assumptions Underlying the Test Statistic
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Twosample ttest :
▫ The samples are independent.
▫ The characteristics of interest in each population
have normal distribution.
▫ The two populations have equal variances.
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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 data measured on nominal or ordinal
scale
▫ Metric data measured on interval or ratio scale
▫ Determine whether single or multiple samples are
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Classification of Univariate
Statistical Techniques
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Overview of Statistical Techniques (Contd.)
Multivariate Techniques
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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
Uses:
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To group variables or people or objects
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To improve the ability to predict variables (such as usage)
To understand relationships between variables (such as
advertising and sales)
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Classification of Multivariate
Statistical Techniques
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Classification of Multivariate Techniques
(Contd.)
Dependence Techniques
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One or more variables can be identified as dependent
variables and the remaining as independent variables
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Choice of dependence technique depends on the number of
dependent variables involved in analysis
Interdependence Techniques
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Whole set of interdependent relationships is examined
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Further classified as having focus on variable or objects
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End of Chapter Sixteen
Marketing Research 12th Edition
