Marketing Research

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Marketing Research

• Aaker, Kumar, Day and Leone
• Tenth Edition
• Instructors Presentation Slides

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Chapter Sixteen
Fundamentals of Data Analysis
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Data Analysis

• A set of methods and techniques used to obtain
  information and insights from data
• Helps avoid erroneous judgments and conclusions
• Can constructively influence the research
  objectives and the research design

• Major Data Preparation techniques
• Data editing
• Coding
• Statistically adjusting the data

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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
• Problems identified with data editing
• Interviewer Error
• Omissions
• Ambiguity
• Inconsistencies
• Lack of Cooperation
• Ineligible Respondent

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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

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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

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Statistically Adjusting the Data (Contd.)

• Variable Re-specification
• 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

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Statistically Adjusting the Data (Contd.)

• Scale Transformation
• 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
• Standardized score,

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Simple Tabulation

• Consists of counting the number of cases that
  fall into various categories

• Uses
• Determine empirical distribution (frequency
  distribution) of the variable in question
• Calculate summary statistics, particularly the
  mean or percentages
• Aid in "data cleaning" aspects

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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

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Frequency Distribution
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Descriptive Statistics

• 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

• 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
• 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

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Factors Influencing the Choice of Statistical
Technique (Contd.)

• Research Design
• Depends on
• Whether dependent or independent samples are used
  
• Number of observations per object
• Number of groups being analyzed
• 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
• Two-sample t-test
• 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
  involved
• For multiple samples, choice of statistical test
  depends on whether the samples are independent or
  dependent

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Classification of Univariate Statistical
Techniques
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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

• Uses
• 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)

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Classification of Multivariate Statistical
Techniques
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Classification of 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
• Interdependence Techniques
• Whole set of interdependent relationships is
  examined
• Further classified as having focus on variable or
  objects