Transcript Marketing Research

Chapter Twelve
Fundamentals of Data Analysis
Preparing the Data for Analysis
• Data editing – the process of identifying
omissions, ambiguities and errors in the
responses
• Coding – process of assigning numerical values to
responses according to a pre-defined system
• Statistically adjusting the data – the process of
modifying the data to enhance its quality for
analysis
– Weighting, transformations, variable re-specification
Preparing the Data for Analysis
Problems Identified With Data Editing
• Omissions
• Ambiguity
• Inconsistencies
• Lack of Cooperation
• Ineligible Respondent
Preparing the Data for Analysis
• Solutions to such problems
Preparing the Data for Analysis
Coding
• closed-ended questions
– Relatively simple and straightforward
• open-ended questions
– Define all possible responses and categorize each
response and then assign a numerical code
– If judgment calls are needed then have several coders
do the same task and check inter-coder reliability
– Inter-coder reliability
Statistical adjustment of data
• Weighting –
– process of enhancing / reducing the importance of
certain data by assigning a number
– Usually done to increase the representativeness of the
sample or achieve study objectives
• Scale transformations
– Manipulation of scales to make them comparable with
other scales e.g. converting lbs to kgs. etc.
– Z-scores (standardized scales)
Preparing the Data for Analysis
• Variable Re-specification
– Existing data modified to create new variables
– Large number of variables collapsed into fewer
variables
– Creates variables that are consistent with research
questions
• Determine if the variable is categorical, rankorder, interval level or ratio level.
Categorical Data Analysis - Objectives
• Describing the sample distribution for the variable
(e.g. gender)
• Frequencies, percentages, quartiles, percentiles, graphs
(bar, line, histogram, pie)
• What are the typical characteristics of the sample?
• Mode
• Does the categorical variable bear any relationship
with a distribution of another categorical variable
(e.g. gender w.r.t. buy the product or not)
• Cross tabs and chi-square as a measure of association
Cross tabulations – example – buyers by age
Under 18
yrs.
19-24 yrs.
25-34 yrs.
Total for
sample
First time
buyers
14%
12.5%
6.6%
11.1%
Brand loyals
21.9%
20%
14.5%
18.9%
Switchers
50%
53%
60%
60%
Never
bought
14.1%
14.5%
18.9%
10%
100%
100%
100%
100%
Distribution of customer types by age: If there were no differences
between age groups, then each age group’s distribution would have
matched the distribution for the total sample.
Crosstabs - conclusions
• The 25-34 yrs. Group is least likely to be first
time buyers than the sample average
• The under 18 year group is more likely to be a
brand loyal than the sample average
Rank order data analysis - Objectives
• What are respondent preferences amongst
several competing alternatives? (e.g. rank your
preferences amongst ten different brands of cars)
– Frequencies, Percentages, Graphs
• What is the typical preference pattern in the
sample (e.g. which car does the sample prefer
the most and which one the least?)
– Mode
Rank order data analysis - Objectives
• Are two sets of respondent preferences
correlated? (e.g. wrist watches brand
preferences with car brand preferences)
– Spearman’s rank correlation coefficient
Interval level / Ratio level data analysis - Objectives
• What is the average response in the sample (e.g.
what is the mean attitude to the brand?)
– Mean / Median
• What is the average variability of the response in
the sample (e.g. On an average, how dispersed
are the sample’s attitudes to the brand from the
mean?)
– Standard deviation
Interval level / Ratio level data analysis - Objectives
• Do two or more subgroups in the sample differ
from each other on the response / differ from a
previously known / hypothesized value
• E.g. do males like the brand significantly more
than the females? T tests, z tests
• E.g. Does attitude to WU vary by student status
(freshman, sophomore, junior, senior)
– ANOVA
Interval level / Ratio level data analysis - Objectives
• Are sample responses on two variables
correlated? (e.g. are sales related to the
advertising expenditure?)
– Pearson correlation
• Can we determine the value of the sample’s
response on a variable, if we know the value on
another variable? (e.g. If we need to achieve 1
million dollars in sales next year, how much
should we spend on advertising?)
– Regression analysis