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• Title Page
• Copyright Page
• Brief Contents
• Contents
• Preface
• Chapter 1 Introduction to Marketing Research
• 1‐1 Marketing Research Is Part of Marketing
• The Philosophy of the Marketing Concept Guides Managers’ Decisions
• Creating the “Right” Marketing Strategy
• 1‐2 What Is Marketing Research?
• Is it Marketing Research or Market Research?
• The Function of Marketing Research
• 1‐3 What Are the Uses of ‐Marketing Research?
• Identifying Market Opportunities and Problems
• Generating, Refining, and Evaluating Potential Marketing Actions
• Selecting Target Markets
• Product Research
• Pricing Research
• Promotion Research
• Distribution Research
• Monitoring Marketing Performance
• Improving Marketing as a Process
• Marketing Research Is Sometimes Wrong
• 1‐4 The Marketing Information System
• Components of an MIS
• Internal Reports System
• Marketing Intelligence System
• Marketing Decision Support System (DSS)
• Marketing Research System
• 1‐5 Job Skills
• Summary
• Key Terms
• Review Questions/Applications
• Case 1.1 Starbucks and Tea Sales
• Case 1.2 Integrated Case: Auto Concepts
• Endnotes
• Chapter 2 The Marketing Research Industry
• 2‐1 Evolution of an Industry
• Earliest Known Studies
• Why Did the Industry Grow?
• The 20th Century Led to a “Mature Industry”
• Marketing Research in the 21st Century
• 2‐2 Who Conducts Marketing Research?
• Client‐Side Marketing Research
• Supply‐Side Marketing Research
• 2‐3 The Industry Structure
• Firm Size by Revenue
• Types of Firms and Their Specialties
• Industry Performance
• 2‐4 Challenges to the Marketing Research Industry
• The Need to Incorporate Innovative and Evolving Sources of Data and Methods
• The Need to Effectively Communicate Insights
• The Need to Hire Talented and Skilled Employees
• 2‐5 Industry Initiatives
• Best Practices
• Maintaining Public Credibility of Research
• Monitoring Industry Trends
• Improving Ethical Conduct
• 2‐6 Industry Standards and Ethics
• Certification of Qualified Research Professionals
• Continuing Education
• 2‐7 A Career in Marketing Research
• Where You’ve Been and Where You’re Headed!
• Summary
• Key Terms
• Review Questions/Applications
• Case 2.1 Pinnacle Research
• Endnotes
• Chapter 3 The Marketing Research Process and Defining the Problem and Research Objectives
• 3‐1 The Marketing Research Process
• The 11‐Step Process
• Caveats to a Step‐by‐Step Process
• Why 11 Steps?
• Not All Studies Use All 11 Steps
• Steps Are Not Always Followed in Order
• Introducing “Where We Are”
• Step 1: Establish the Need for Marketing Research
• The Information Is Already Available
• The Timing Is Wrong
• Costs Outweigh the Value
• Step 2: Define the Problem
• Step 3: Establish Research Objectives
• Step 4: Determine Research Design
• Step 5: Identify Information Types and Sources
• Step 6: Determine Methods of Accessing Data
• Step 7: Design Data Collection Forms
• Step 8: Determine the Sample Plan and Size
• Step 9: Collect Data
• Step 10: Analyze Data
• Step 11: Communicate the Insights
• 3‐2 Defining the Problem
• 1. Recognize the Problem
• Failure to Meet an Objective
• Identification of an Opportunity
• 2. Understand the Background of the Problem
• Conduct a Situation Analysis
• Clarify the Symptoms
• Determine the Probable Causes of the Symptom(s)
• 3. Determine the Decision Alternatives
• 4. Formulate the Problem Statement
• 3‐3 Research Objectives
• Using Hypotheses
• Defining Constructs
• 3‐4 Action Standards
• Impediments to Problem Definition
• 3‐5 The Marketing Research Proposal
• Ethical Issues and the Research Proposal
• Summary
• Key Terms
• Review Questions/Applications
• Case 3.1 Aging Population in Malaysia
• Case 3.2 Integrated Case: Auto Concepts
• Endnotes
• Chapter 4 Research Design
• 4‐1 Research Design
• Why Is Knowledge of Research Design Important?
• 4‐2 Three Types of Research Design
• Research Design: A Caution
• 4‐3 Exploratory Research
• Uses of Exploratory Research
• Gain Background Information
• Define Terms
• Clarify Problems and Hypotheses
• Establish Research Priorities
• Methods of Conducting Exploratory Research
• Secondary Data Analysis
• Experience Surveys
• Case Analysis
• Focus Groups
• 4‐4 Descriptive Research
• Classification of Descriptive Research Studies
• 4‐5 Causal Research
• Experiments
• Experimental Design
• Before‐After Testing
• A/B Testing
• How Valid Are Experiments?
• Types of Experiments
• 4‐6 Test Marketing
• Types of Test Markets
• Standard Test Market
• Controlled Test Markets
• Simulated Test Markets
• Selecting Test‐Market Regions
• Pros and Cons of Test Marketing
• Summary
• Key Terms
• Review Questions/Applications
• Case 4.1 Memos from a Researcher
• Case 4.2 Analysis of Coffee Segments with Nielsen Panel Data
• Endnotes
• Chapter 5 Secondary Data and Packaged Information
• 5‐1 Big Data
• 5‐2 Primary Versus Secondary Data
• Uses of Secondary Data
• 5‐3 Classification of Secondary Data
• Internal Secondary Data
• External Secondary Data
• Published Sources
• Official Statistics
• Data Aggregators
• 5‐4 Advantages and Disadvantages of Secondary Data
• Advantages of Secondary Data
• Disadvantages of Secondary Data
• Incompatible Reporting Units
• Mismatched Measurement Units
• Unusable Class Definitions
• Outdated Data
• 5‐5 Evaluating Secondary Data
• What Was the Purpose of the Study?
• Who Collected the Information?
• What Information Was Collected?
• How Was the Information Obtained?
• How Consistent Is the Information with Other Information?
• 5‐6 What Is Packaged Information?
• Syndicated Data
• Packaged Services
• 5‐7 Advantages and Disadvantages of Packaged Information
• Syndicated Data
• Packaged Services
• 5‐8 Applications of Packaged Information
• Measuring Consumer Attitudes and Opinions
• Identifying Segments
• Monitoring Media Usage and Promotion Effectiveness
• Tracking Sales
• 5‐9 Digital Tracking Data
• 5‐10 Social Media Data
• Types of Social Media Information
• Reviews
• Tips
• New Uses
• Competitor News
• Advantages and Disadvantages of Social Media Data
• Tools to Monitor Social Media
• 5‐11 Internet of Things
• 5‐12 Big Data and Ethics
• Summary
• Key Terms
• Review Questions/Applications
• Case 5.1 The Men’s Market for Athleisure
• Case 5.2 Analyzing the Coffee Category with POS ‐Syndicated Data
• Endnotes
• Chapter 6 Qualitative Research Techniques
• 6‐1 Quantitative, Qualitative, and Mixed Methods Research
• Types of Mixed Methods
• 6‐2 Observation Techniques
• Types of Observation
• Direct Versus Indirect
• Covert Versus Overt
• Structured Versus Unstructured
• In Situ Versus Invented
• Appropriate Conditions for the Use of Observation
• Advantages of Observational Data
• Limitations of Observational Data
• 6‐3 Focus Groups
• How Focus Groups Work
• Online Focus Groups
• Operational Aspects of Traditional Focus Groups
• How Many People Should Be in a Focus Group?
• Who Should Be in the Focus Group?
• How Many Focus Groups Should Be Conducted?
• How Should Focus Group Participants Be Recruited and Selected?
• Where Should a Focus Group Meet?
• When Should the Moderator Become Involved in the Research Project?
• How Are Focus Group Results Used?
• What Other Benefits Do Focus Groups Offer?
• Advantages of Focus Groups
• Disadvantages of Focus Groups
• When Should Focus Groups Be Used?
• When Should Focus Groups Not Be Used?
• Some Objectives of Focus Groups
• 6‐4 Ethnographic Research
• Mobile Ethnography
• Netnography
• 6‐5 Marketing Research Online Communities
• 6‐6 Other Qualitative Research Techniques
• In‐Depth Interviews
• Protocol Analysis
• Projective Techniques
• Word‐Association Test
• Sentence‐Completion Test
• Picture Test
• Cartoon or Balloon Test
• Role‐Playing Activity
• Neuromarketing
• Neuroimaging
• Eye Tracking
• Facial Coding
• The Controversy
• Still More Qualitative Techniques
• 6‐7 The Analysis of Qualitative Data
• Steps for Analyzing Qualitative Data
• Using Electronic Tools to Analyze Qualitative Data
• Summary
• Key Terms
• Review Questions/Applications
• Case 6.1 Mumuni Advertising Agency
• Case 6.2 Integrated Case: Auto Concepts
• Endnotes
• Chapter 7 Evaluating Survey Data Collection Methods
• 7‐1 Advantages of Surveys
• 7‐2 Modes of Data Collection
• Data Collection and Impact of Technology
• Person‐Administered Surveys
• Advantages of Person‐Administered Surveys
• Disadvantages of Person‐Administered Surveys
• Computer‐Assisted, Person‐Administered Surveys
• Advantages of Computer‐Assisted Surveys
• Disadvantages of Computer‐Assisted Surveys
• Self‐Administered Surveys
• Advantages of Self‐Administered Surveys
• Disadvantages of Self‐Administered Surveys
• Computer‐Administered Surveys
• Advantages of Computer‐Administered Surveys
• Disadvantage of Computer‐Administered Surveys
• Mixed‐Mode Surveys
• Advantage of Mixed‐Mode Surveys
• Disadvantages of Mixed‐Mode Surveys
• 7‐3 Descriptions of Data Collection Methods
• Person‐Administered/Computer‐Assisted Interviews
• In‐Home Surveys
• Mall‐Intercept Surveys
• In‐Office Surveys
• Telephone Surveys
• Computer‐Administered Interviews
• Fully Automated Survey
• Online Surveys
• Self‐Administered Surveys (Without Computer Presence)
• Group Self‐Administered Survey
• Drop‐Off Survey
• Mail Survey
• 7‐4 Working with a Panel Company
• Advantages of Using a Panel Company
• Fast Turnaround
• High Quality
• Database Information
• Targeted Respondents
• Integrated Features
• Disadvantages of Using a Panel Company
• Not Random Samples
• Overused Respondents
• Cost
• Top Panel Companies
• 7‐5 Choosing the Survey Method
• How Fast Is the Data Collection?
• How Much Does the Data Collection Cost?
• How Good Is the Data Quality?
• Other Considerations
• Summary
• Key Terms
• Review Questions/Applications
• Case 7.1 Whale Watching Tourism in Australia
• Case 7.2 Food Waste Research
• Endnotes
• Chapter 8 Understanding Measurement, Developing Questions, and Designing the Questionnaire
• 8‐1 Basic Measurement Concepts
• 8‐2 Types of Measures
• Nominal Measures
• Ordinal Measures
• Scale Measures
• 8‐3 Interval Scales Commonly Used in Marketing Research
• The Likert Scale
• The Semantic Differential Scale
• The Stapel Scale
• Slider Scales
• Two Issues with Interval Scales Used in Marketing Research
• The Scale Should Fit the Construct
• 8‐4 Reliability and Validity of Measurements
• 8‐5 Designing a Questionnaire
• The Questionnaire Design Process
• 8‐6 Developing Questions
• Four Do’s of Question Wording
• The Question Should Be Focused on a Single Issue or Topic
• The Question Should Be Brief
• The Question Should Be Grammatically Simple
• The Question Should Be Crystal Clear
• Four Do Not’s of Question Wording
• Do Not “Lead” the Respondent to a Particular Answer
• Do Not Use “Loaded” Wording or Phrasing
• Do Not Use a “Double‐Barreled” Question
• Do Not Use Words That Overstate the Case
• 8‐7 Questionnaire Organization
• The Introduction
• Who Is Doing the Survey?
• What Is the Survey About?
• How Did You Select Me?
• Motivate Me to Participate
• Am I Qualified to Take Part?
• Question Flow
• 8‐8 Computer‐Assisted Questionnaire Design
• Question Creation
• Skip and Display Logic
• Data Collection and Creation of Data Files
• Ready‐Made Respondents
• Data Analysis, Graphs, and Downloading Data
• 8‐9 Finalize the Questionnaire
• Coding the Questionnaire
• Pretesting the Questionnaire
• Summary
• Key Terms
• Review Questions/Applications
• Case 8.1 Extreme Exposure Rock Climbing Center Faces The Krag
• Case 8.2 Integrated Case: Auto Concepts
• Endnotes
• Chapter 9 Selecting the Sample
• 9‐1 Basic Concepts in Samples and Sampling
• Population
• Census
• Sample and Sample Unit
• Sample Frame and Sample Frame Error
• Sampling Error
• 9‐2 Why Take a Sample?
• 9‐3 Probability Versus Nonprobability Sampling Methods
• 9‐4 Probability Sampling Methods
• Simple Random Sampling
• The Random Device Method
• The Random Numbers Method
• Advantages and Disadvantages of Simple Random Sampling
• Simple Random Sampling Used In Practice
• Systematic Sampling
• Why Systematic Sampling Is “Fair”
• Disadvantage of Systematic Sampling
• Cluster Sampling
• Area Sampling as a Form of Cluster Sampling
• Disadvantage of Cluster (Area) Sampling
• Stratified Sampling
• Working with Skewed Populations
• Accuracy of Stratified Sampling
• How to Apply Stratified Sampling
• 9‐5 Nonprobability Sampling Methods
• Convenience Samples
• Chain Referral Samples
• Purposive Samples
• Quota Samples
• 9‐6 Online Sampling Techniques
• Online Panel Samples
• River Samples
• Email List Samples
• 9‐7 Developing a Sample Plan
• Summary
• Key Terms
• Review Questions/Applications
• Case 9.1 Peaceful Valley Subdivision: Trouble in Suburbia
• Case 9.2 Jet’s Pets
• Endnotes
• Chapter 10 Determining the Size of a Sample
• 10‐1 Sample Size Axioms
• 10‐2 The Confidence Interval Method of Determining Sample Size
• Sample Size and Accuracy
• P and Q: The Concept of Variability
• The Concept of a Confidence Interval
• How Population Size (N) Affects Sample Size
• 10‐3 The Sample Size Formula
• Determining Sample Size via the Confidence Interval Formula
• Variability: p X q
• Acceptable Margin of Sample Error: e
• Level of Confidence: z
• 10‐4 Practical Considerations in Sample Size Determination
• How to Estimate Variability in the Population
• How to Determine the Amount of Acceptable Sample Error
• How to Decide on the Level of Confidence
• How to Balance Sample Size with the Cost of Data Collection
• 10‐5 Other Methods of Sample Size Determination
• Arbitrary “Percent Rule of Thumb” Sample Size
• Conventional Sample Size Specification
• “Credibility Interval” Approach to Sample Size
• Statistical Analysis Requirements in Sample Size Specification
• Cost Basis of Sample Size Specification
• 10‐6 Three Special Sample Size Determination Situations
• Sampling from Small Populations
• Sample Size Using Nonprobability Sampling
• Sampling from Panels
• Summary
• Key Terms
• Review Questions/Applications
• Case 10.1 Target: Deciding on the Number of Telephone Numbers
• Case 10.2 Bounty Paper Towels
• Endnotes
• Chapter 11 Dealing with Fieldwork and Data Quality Issues
• 11‐1 Data Collection and Nonsampling Error
• 11‐2 Possible Errors in Field Data Collection
• Intentional Fieldworker Errors
• Unintentional Fieldworker Errors
• Intentional Respondent Errors
• Unintentional Respondent Errors
• 11‐3 Field Data Collection Quality Controls
• Control of Intentional Fieldworker Error
• Control of Unintentional Fieldworker Error
• Control of Intentional Respondent Error
• Control of Unintentional Respondent Error
• Final Comment on the Control of Data Collection Errors
• 11‐4 Nonresponse Error
• Refusals to Participate in the Survey
• Break‐Offs During the Interview
• Refusals to Answer Specific Questions (Item Omission)
• What Is a Completed Interview?
• Measuring Response Rate in Surveys
• 11‐5 Ways Panel Companies Control Error
• 11‐6 Dataset, Coding Data, and the Data Code Book
• 11‐7 Data Quality Issues
• What to Look for in Raw Data Inspection
• Incomplete Response
• Nonresponses to Specific Questions (Item Omissions)
• Yea‐ or Nay‐Saying Patterns
• Middle‐of‐the‐Road Patterns
• Other Data Quality Problems
• How to Handle Data Quality Issues
• Summary
• Key Terms
• Review Questions/Applications
• Case 11.1 Alert! Squirt
• Case 11.2 Sony Televisions LED 4K Ultra HD HDR Smart TV Survey
• Endnotes
• Chapter 12 Using Descriptive Analysis, Performing Population Estimates, and Testing Hypotheses
• 12‐1 Types of Statistical Analyses Used in Marketing Research
• Descriptive Analysis
• Inference Analysis
• Difference Analysis
• Association Analysis
• Relationships Analysis
• 12‐2 Understanding Descriptive Analysis
• Measures of Central Tendency: Summarizing the “Typical” Respondent
• Mode
• Median
• Mean
• Measures of Variability: Relating the Diversity of Respondents
• Frequency and Percentage Distribution
• Range
• Standard Deviation
• 12‐3 When to Use Each Descriptive Analysis Measure
• 12‐4 The Auto Concepts Survey: Obtaining Descriptive Statistics with SPSS
• Integrated Case The Auto Concepts Survey: Obtaining Descriptive Statistics with SPSS
• Use SPSS to Open Up and Use the Auto Concepts Dataset
• Obtaining a Frequency Distribution and the Mode with SPSS
• Finding the Median with SPSS
• Finding the Mean, Range, and Standard Deviation with SPSS
• 12‐5 Reporting Descriptive Statistics to Clients
• Reporting Scale Data (Ratio and Interval Scales)
• Reporting Nominal or Categorical Data
• 12‐6 Statistical Inference: Sample Statistics and Population Parameters
• 12‐7 Parameter Estimation: Estimating the Population Percentage or Mean
• Sample Statistic
• Standard Error
• Confidence Interval
• How to Interpret an Estimated Population Mean or Percentage Range
• 12‐8 The Auto Concepts Survey: How to Obtain and Use a Confidence Interval for a Mean with SPSS
• 12‐9 Reporting Confidence Intervals to Clients
• 12‐10 Hypothesis Tests
• Test of the Hypothesized Population Parameter Value
• Auto Concepts: How to Use SPSS to Test a Hypothesis for a Mean
• 12‐11 Reporting Hypothesis Tests to Clients
• Summary
• Key Terms
• Review Questions/Applications
• Case 12.1 L’Experience Restaurant Survey Descriptive and ‐Inference Analysis
• Case 12.2 Integrated Case: Auto Concepts Descriptive and ‐Inference Analysis
• Endnotes
• Chapter 13 Implementing Basic Differences Tests
• 13‐1 Why Differences Are Important
• 13‐2 Small Sample Sizes: The Use of a t Test or z Test and How SPSS Eliminates the Worry
• 13‐3 Testing for Significant Differences Between Two Groups
• Differences Between Percentages with Two Groups (Independent Samples)
• How to Use SPSS for Differences Between Percentages of Two Groups
• Differences Between Means with Two Groups (Independent Samples)
• Integrated Case The Auto Concepts Survey: How to Perform an Independent Sample Significance of Dif
• 13‐4 Testing for Significant Differences in Means Among More Than Two Groups: Analysis of Variance
• Basics of Analysis of Variance
• Post Hoc Tests: Detect Statistically Significant Differences Among Group Means
• Integrated Case Auto Concepts: How to Run Analysis of Variance on SPSS
• Interpreting ANOVA (Analysis of Variance)
• 13‐5 Reporting Group Differences Tests to Clients
• 13‐6 Differences Between Two Means Within the Same Sample (Paired Sample)
• Integrated Case The Auto Concepts Survey: How to Perform a Paired Samples t Test Significance of D
• 13‐7 Null Hypotheses for Differences Tests Summary
• Summary
• Key Terms
• Review Questions/Applications
• Case 13.1 L’Experience Restaurant Survey Differences Analysis
• Case 13.2 Integrated Case: The Auto Concepts Survey ‐Differences Analysis
• Endnotes
• Chapter 14 Making Use of Associations Tests
• 14‐1 Types of Relationships (Associations) Between Two Variables
• Linear and Curvilinear Relationships
• Monotonic Relationships
• Nonmonotonic Relationships
• 14‐2 Characterizing Relationships Between Variables
• Presence
• Pattern
• Strength of Association
• 14‐3 Correlation Coefficients and Covariation
• Rules of Thumb for Correlation Strength
• The Correlation Sign: The Direction of the Relationship
• Visualizing Covariation using Scatter Diagrams
• 14‐4 The Pearson Product Moment Correlation Coefficient
• Integrated Case Auto Concepts: How to Obtain Pearson Product Moment Correlation(s) with SPSS
• 14‐5 Reporting Correlation Findings to Clients
• 14‐6 Cross‐Tabulations
• Cross‐Tabulation Analysis
• Types of Frequencies and Percentages in a Cross‐Tabulation Table
• 14‐7 Chi‐Square Analysis
• Observed and Expected Frequencies
• The Computed x2 Value
• The Chi‐Square Distribution
• How to Interpret a Chi‐Square Result
• Integrated Case Auto Concepts: Analyzing Cross‐Tabulations for Significant Associations by Perfo
• 14‐8 Chi‐Square Test of Proportions: A Useful Variation of Cross‐Tabulation Analysis
• 14‐9 Communicating Cross‐Tabulation Insights to Clients: Use Data Visualization
• 14‐10 Special Considerations In Association Procedures
• Summary
• Key Terms
• Review Questions/Applications
• Case 14.1 L’Experience Restaurant Survey Associative Analysis
• Case 14.2 Integrated Case: The Auto Concepts Survey Associative Analysis
• Endnotes
• Chapter 15 Understanding Regression Analysis Basics
• 15‐1 Bivariate Linear Regression Analysis
• Basic Concepts in Regression Analysis
• Independent and Dependent Variables
• Computing the Slope and the Intercept
• How to Improve a Regression Analysis Finding
• 15‐2 Multiple Regression Analysis
• An Underlying Conceptual Model
• Multiple Regression Analysis Described
• Basic Assumptions in Multiple Regression
• Integrated Case Auto Concepts: How to Run and Interpret Multiple Regression Analysis on SPSS
• “Trimming” the Regression for Significant Findings
• 15‐3 Special Uses of Multiple Regression Analysis
• Using a “Dummy” Independent Variable
• Using Standardized Betas to Compare the Importance of ‐Independent Variables
• Using Multiple Regression as a Screening Device
• Interpreting the Findings of Multiple Regression Analysis
• 15‐4 Stepwise Multiple Regression
• How to Do Stepwise Multiple Regression with SPSS
• Step‐by‐Step Summary of How to Perform Multiple Regression Analysis
• 15‐5 Warnings Regarding Multiple Regression Analysis
• 15‐6 Communicating Regression Analysis Insights to Clients
• Summary
• Key Terms
• Review Questions/Applications
• Case 15.1 L’Experience Restaurant Survey Regression Analysis
• Case 15.2 Integrated Case: Auto Concepts Segmentation Analysis
• Endnotes
• Chapter 16 Communicating Insights
• Use Effective Communication Methods
• Communicate Actionable, Data‐Supported Strategies
• Disseminate Insights Throughout the Organization
• 16‐1 Characteristics of Effective Communication
• Accuracy
• Clarity
• Memorability
• Actionability
• Style
• 16‐2 Avoid Plagiarism!
• 16‐3 Videos, Infographics, and Immersion Techniques
• Videos
• Infographics
• Immersion Techniques
• 16‐4 The Traditional Marketing Research Report
• 16‐5 Know Your Audience
• 16‐6 Elements of the Marketing Research Report
• Front Matter
• Title Page
• Letter of Authorization
• Letter/Memo of Transmittal
• Table of Contents
• List of Illustrations
• Abstract/Executive Summary
• Body
• Introduction
• Research Objectives
• Method
• Method or Methodology?
• Results
• Limitations
• Conclusions and Recommendations
• End Matter
• 16‐7 Guidelines and Principles for the Written Report
• Headings and Subheadings
• Visuals
• Style
• 16‐8 Using Visuals: Tables and Figures
• Tables
• Pie Charts
• Bar Charts
• Line Graphs
• Flow Diagrams
• Producing an Appropriate Visual
• 16‐9 Presenting Your Research Orally
• 16‐10 Data Visualization Tools and Dashboards
• 16‐11 Disseminating Insights Throughout an Organization
• Summary
• Key Terms
• Review Questions/Applications
• Case 16.1 Integrated Case: Auto Concepts: Report Writing
• Case 16.2 Integrated Case: Auto Concepts: Making a PowerPoint Presentation
• Case 16.3 How Marketing Research Data Can Begin with a Sketch
• Endnotes
• Name Index
• A
• B
• C
• D
• E
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• G
• H
• I
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• M
• N
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• P
• Q
• R
• S
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• X
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• Z
• Subject Index
• A
• B
• C
• D
• E
• F
• G
• H
• I
• J
• K
• L
• M
• N
• O
• P
• Q
• R
• S
• T
• U
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• X
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• Z
• Selected Formulas