Test Bank for Marketing Research, 12th Edition by Carl McDaniel

Test Bank for Marketing Research 12e 12th Edition by Carl McDaniel, Roger Gates. ISBN 6310. Full Chapters test bank included 1 Steps in Creating Market Insights and the Growing Role of Marketing Analytics 1 Marketing Research and Developing Market Insights 1 Marketing Research Defined 2 Importance of Marketing Research to Management 2 Understanding the Ever-Changing Marketplace 3 Social Media and User-Generated Content 3 Proactive Role of Marketing Research 4 Marketing Analytics Moves to the Forefront 4 The Research Process 4 Recognize the Problem or Opportunity 5 Find Out Why the Information is Being Sought 6 Understand the Decision-Making Environment with Exploratory Research 6 Use the Symptoms to Clarify the Problem 8 Translate the Management Problem into a Marketing Research Problem 9 Determine Whether the Information Already Exists 9 Determine Whether the Question Can Be Answered 10 State the Research Objectives 10 Research Objectives As Hypotheses 11 Marketing Research Process 11 Creating the Research Design 11 Choosing a Basic Method of Research 11 Selecting the Sampling Procedure 13 Collecting the Data 13 Analyzing the Data 13 Presenting the Report 14 Following Up 14 Managing the Research Process 14 The Research Request 14 Request for Proposal 15 The Marketing Research Proposal 16 What to Look for in a Marketing Research Supplier 17 Modifying the Research Process—Marketing Analytics, Big Data, and Unsupervised Learning 17 A Shifting Paradigm 18 What Motivates Decision Makers to Use Research Information? 18 Summary 19 Key Terms 19 Questions for Review & Critical Thinking 20 Working the Net 20 Real-Life Research 1.1: Can Anyone Be a Market Researcher? 21 2 Secondary Data: A Potential Big Data Input 23 Nature of Secondary Data 23 Advantages of Secondary Data 24 Limitations of Secondary Data 25 Internal Databases 27 Creating an Internal Database 27 First, Second, and Third Party Data 27 Behavioral Targeting 28 Big Data 29 The Big Data Breakthrough 29 Making Big Data Actionable in Traditional Marketing Research Environments 30 Battle over Privacy 31 The Federal Trade Commission 32 State Data Privacy Laws 32 The General Data Protection Regulation 32 Summary 33 Key Terms 34 Questions for Review & Critical Thinking 34 Working the Net 34 Real-Life Research 2.1: The GDPR and American Small Business 34 3 Measurement to Build Marketing Insight 36 Measurement Process 36 Step One: Identify the Concept of Interest 37 Step Two: Develop a Construct 38 Step Three: Define the Concept Constitutively 38 Step Four: Define the Concept Operationally 38 Step Five: Develop a Measurement Scale 40 Nominal Level of Measurement 41 Ordinal Level of Measurement 41 Interval Level of Measurement 42 Ratio Level of Measurement 42 Step Six: Evaluate the Reliability and Validity of the Measurement 43 Reliability 45 Validity 47 Reliability and Validity—A Concluding Comment 51 Attitude Measurement Scales 51 Graphic Rating Scales 52 Itemized Rating Scales 53 Traditional One-Stage Format 55 Two-Stage Format 55 Rank-Order Scales 56 Paired Comparisons 56 Constant Sum Scales 56 Semantic Differential Scales 58 Stapel Scales 59 Likert Scales 60 Purchase-Intent Scales 62 Scale Conversions 64 Net Promoter Score (NPS) 65 Considerations in Selecting a Scale 66 The Nature of the Construct Being Measured 66 Type of Scale 67 Balanced versus Nonbalanced Scale 67 Number of Scale Categories 67 Forced versus Nonforced Choice 68 Summary 68 Key Terms 69 Questions for Review & Critical Thinking 70 Working the Net 70 Real-Life Research 3.1: PNC Bank Considers Changing Its Customer Satisfaction Measurement Scale 71 4 Acquiring Data Via a Questionnaire 73 Role of a Questionnaire 73 Criteria for a Good Questionnaire 74 Does It Provide the Necessary Decision-Making Information? 74 Does It Consider the Respondent? 75 Does It Meet Editing Requirements? 75 Does It Solicit Information in an Unbiased Manner: Questionnaire Design Process 76 Step One: Determine Survey Objectives, Resources, and Constraints 77 Step Two: Determine the Data-Collection Method 78 Step Three: Determine the Question Response Format 78 Step Four: Decide on the Question Wording 81 Step Five: Establish Questionnaire Flow and Layout 84 Step Six: Evaluate the Questionnaire 87 Step Seven: Obtain Approval of All Relevant Parties 88 Step Eight: Pretest and Revise 88 Step Nine: Prepare Final Questionnaire Copy 88 Step Ten: Implement the Survey 88 Field Management Companies 89 Avoiding Respondent Fatigue 89 Intelligence Moves Into Questionnaire Coding 90 Conducting Surveys on Smartphones and Tablets 91 The Rapid Growth of Do-It-Yourself (DIY) Surveys 92 Summary 93 Key Terms 94 Questions for Review & Critical Thinking 94 Working the Net 95 Real-Life Research 4.1: Arrow Cleaners 95 5 Sample Design 99 Concept of Sampling 100 Population 100 Sample versus Census 101 Developing a Sampling Plan 101 Step One: Define the Population of Interest 101 Step Two: Choose a Data-Collection Method 104 Step Three: Identify a Sampling Frame 104 Step Four: Select a Sampling Method 104 Step Five: Determine Sample Size 106 Step Six: Develop Operational Procedures for Selecting Sample Elements 106 Step Seven: Execute the Operational Sampling Plan 106 Sampling and Nonsampling Errors 106 Probability Sampling Methods 107 Simple Random Sampling 107 Systematic Sampling 108 Stratified Sampling 109 Cluster Sampling 110 Nonprobability Sampling Methods 111 Convenience Samples 111 Judgment Samples 111 Quota Samples 112 Snowball Samples 112 Internet Sampling 112 Determining Sample Size 113 Determining Sample Size for Probability Samples 113 Budget Available 113 Rule of Thumb 114 Number of Subgroups Analyzed 114 Traditional Statistical Methods 115 Normal Distribution 115 General Properties 115 Basic Concepts 116 Making Inferences on the Basis of a Single Sample 118 Point and Interval Estimates 118 Sampling Distribution of the Proportion 119 Determining Sample Size 120 Problems Involving Means 120 Problems Involving Proportions 122 Determining Sample Size for Stratified and Cluster Samples 123 Sample Size for Qualitative Research 123 Population Size and Sample Size 124 Summary 125 Key Terms 126 Questions for Review & Critical Thinking 126 Working the Net 127 Real-Life Research 5.1: Insights Research Group (IRG) 127 6 Traditional Survey Research 129 Why Decision Makers Like Survey Research 129 Types of Errors in Survey Research 130 Sampling Error 130 Systematic Error 131 Types of Surveys 135 Door-to-Door Interviews 135 Executive Interviews 136 Mall-Intercept Interviews 136 Telephone Interviews 137 Self-Administered Questionnaires 138 Mail Surveys 139 Determination of the Survey Method 141 Sampling Precision 141 Budget 141 Requirements for Respondent Reactions 142 Quality of Data 142 Length of the Questionnaire 142 Incidence Rate 143 Structure of the Questionnaire 143 Time Available to Complete the Survey 143 Summary 144 Key Terms 144 Questions for Review & Critical Thinking 145 Real-Life Research 6.1: Do Consumers Like Chatbots? 145 7 Qualitative Research 146 Nature of Qualitative Research 146 Qualitative Research versus Quantitative Research 147 The Use of Qualitative Research 147 Limitations of Qualitative Research 148 Focus Groups 149 Popularity of Focus Groups 149 Conducting Focus Groups 150 Focus Group Trends 157 Benefits and Drawbacks of Focus Groups 158 Other Qualitative Methodologies 159 Individual Depth Interviews 159 Projective Tests 163 Summary 167 Key Terms 167 Questions for Review & Critical Thinking 167 Working the Net 168 Real-Life Research 7.1: A Sound Approach for the Sound 168 8 Online Marketing Research: The Growth of Mobile and Social Media Research 171 Using the Internet for Secondary Data 172 Online Qualitative Research 172 Online Bulletin Boards 172 Webcam and Streaming Technology Focus Groups 173 Using the Internet to Find Online Participants 174 Online Individual Depth Interviews (IDIs) 175 Online Survey Research 175 Advantages of Online Surveys 175 Disadvantages of Online Surveys 176 Tools for Conducting Online Surveys 177 Commercial Online Panels 178 Panel Recruitment 178 Open Recruitment 178 Closed Recruitment 179 Respondent Participation 179 Panel Management 180 Mobile Internet Research—The Future is Now 180 Advantages of Mobile 181 Designing a Mobile Survey 181 Social Media Marketing Research 182 Summary 182 Key Terms 183 Questions for Review & Critical Thinking 183 Working the Net 183 Real-Life Research 8.1: Shoppers Spending More In-Store Than Online 183 9 Primary Data Collection: Observation 185 Nature of Observation Research 185 Conditions for Using Observation 186 Approaches to Observation Research 186 Advantages of Observation Research 188 Disadvantages of Observation Research 189 Human Observation 189 Ethnographic Research 189 Mobile Ethnography 192 Mystery Shoppers 192 One-Way Mirror Observations 194 Machine Observation 194 Neuromarketing 194 Facial Action Coding Services (FACS) 197 Gender and Age Recognition Systems 199 In-Store Tracking 199 Television and Video Audience Measurement and Tracking 200 Symphony IRI Consumer Network 200 Tracking 201 Magazines Track Online Readers and Apply It Also to Print 201 Social Media Tracking 202 Virtual Reality and Augmented Reality Marketing Research 204 Summary 204 Key Terms 205 Questions for Review & Critical Thinking 205 Working the Net 206 Real-Life Research 9.1: Bausch & Lomb Fine-Tune the Details 206 10 Marketing Analytics 208 What is Marketing Analytics? 209 The Marketing Analytics Process 210 Getting the Data 210 Big Data Sources 210 Data from Traditional Sources 211 Organizing, Merging, and Using Big Data 212 Acting on Results of Analysis 212 Big Data 212 Background on Big Data Issues 212 How Does It Work? 213 Analyzing Data: Descriptive, Predictive, and Prescriptive Analytics 214 Descriptive Analytics 214 Predictive Analytics 214 Prescriptive Analytics 215 Advanced Analytical Methods 216 Data Mining 216 Machine and Deep Learning 219 Artificial Intelligence or AI 220 Data Visualization 224 Infographics 225 Marketing Dashboards 225 Privacy Issues 226 Privacy versus Customization 226 Summary 228 Key Terms 229 Questions for Review & Critical Thinking 229 Working the Net 230 Real-Life Research 10.1: Affiliated Parking Systems Looks to New Pricing Approach 230 11 Primary Data: Experimentation and Test Markets 231 What is an Experiment? 232 Demonstrating Causation 232 Concomitant Variation 233 Appropriate Time Order of Occurrence 233 Elimination of Other Possible Causal Factors 233 Experimental Setting 234 Laboratory Experiments 234 Field Experiments 234 Experimental Validity 234 Experimental Notation 235 Extraneous Variables 235 Examples of Extraneous Variables 236 Controlling Extraneous Variables 237 Experimental Design, Treatment, and Effects 238 Limitations of Experimental Research 239 High Cost 239 Security Issues 239 Implementation Problems 239 Selected Experimental Designs 240 Preexperimental Designs 240 True Experimental Designs 241 Quasi-Experiments 242 Test Markets 244 Types of Test Markets 245 Decision to Conduct Test Marketing 248 Steps in a Test Market Study 249 Summary 252 Key Terms 252 Questions for Review & Critical Thinking 253 Working the Net 254 Real-Life Research 11.1: Los Lobos Beer 254 12 Data Processing and Basic Data Analysis 255 Overview of Data Analysis Procedure for Survey Research 256 Step One: Validation and Editing of Paper Surveys 256 Validation 256 Quality Assurance for Internet Panels 257 Quality Assurance—Respondent Cooperation and Attention Issues 258 Special Issues with Big Data 260 Editing 260 Step Two: Coding 264 Coding Process 265 Automated Coding Systems and Text Processing 266 Intelligent Capture Systems 267 The Data Capture Process 268 Scanning 268 Step Four: Logical Cleaning of Data 269 Step Five: Tabulation and Statistical Analysis 269 One-Way Frequency Tables 269 Cross Tabulations 272 Death of Crosstabs? 274 Graphic Representations of Data 274 Line Charts 275 Pie Charts 275 Bar Charts 275 Descriptive Statistics 278 Measures of Central Tendency 278 Measures of Dispersion 279 Percentages and Statistical Tests 280 Summary 281 Key Terms 281 Questions for Review & Critical Thinking 282 Working the Net 284 Real-Life Research 12.1: Buzzy’s Tacos 284 13 Statistical Testing of Differences and Relationships 285 Evaluating Differences and Changes 286 Statistical Significance 286 Hypothesis Testing 287 Steps in Hypothesis Testing 288 Types of Errors in Hypothesis Testing 290 Accepting H0 versus Failing to Reject (FTR) H0 292 One-Tailed versus Two-Tailed Test 292 Example of Performing a Statistical Test 292 Commonly Used Statistical Hypothesis Tests 295 Independent versus Related Samples 295 Degrees of Freedom 295 Goodness of Fit 296 Chi-Square Test 296 Hypotheses about One Mean 299 t Test 299 Hypotheses about Two Means 300 Hypotheses about Proportions 302 Proportion in One Sample 302 Two Proportions in Independent Samples 303 Analysis of Variance (ANOVA) 305 p Values and Significance Testing 308 Summary 309 Key Terms 309 Questions for Review & Critical Thinking 310 Working the Net 311 Real-Life Research 13.1: Analyzing William D. Scott (WDS) Segmentation Results 312 14 More Powerful Statistical Methods 313 Data Scientist—Hot New Career 313 Bivariate Statistical Analysis 314 Bivariate Analysis of Relationships 314 Bivariate Regression 314 Nature of the Relationship 315 Example of Bivariate Regression 316 Correlation for Metric Data: Pearson’s Product–Moment Correlation 322 Multivariate Analysis Procedures 323 Multivariate Software 324 Multiple Regression Analysis 324 Applications of Multiple Regression Analysis 325 Multiple Regression Analysis Measures 326 Dummy Variables 327 Potential Use and Interpretation Problems 327 Multiple Discriminant Analysis 328 Applications of Multiple Discriminant Analysis 329 Cluster Analysis 330 Procedures for Clustering 330 Applications of Cluster Analysis 331 Factor Analysis 332 Factor Scores 332 Factor Loadings 334 Naming Factors 334 Number of Factors to Retain 335 Conjoint Analysis 335 Simulating Buyer Choice 335 Limitations of Conjoint Analysis 336 Neural Networks 337 Description of a Neural Network 337 How Neural Networks “Learn” 338 When Neural Networks Are Appropriate 338 Limitations of Neural Networks 338 Predictive Analytics 339 Using Predictive Analytics 339 Privacy Concerns and Ethics 341 Commercial Predictive Modeling Software and Applications 341 Summary 341 Key Terms 342 Questions for Review & Critical Thinking 343 Working the Net 345 Real-Life Research 14.1: Satisfaction Research for Pizza Pronto 345 15 Communicating Analytics and Research Insights 347 The Research Report 347 Organizing the Report 348 Format of the Report 349 Formulating Recommendations 349 Presenting the Results 355 Making a Presentation 356 Infographics 356