Marketing Research
* The Research in Action feature links the concepts discussed in the chapter to actual industry practice* The case study at the end of each chapter acquaints learners with a variety of organizational scenarios that they may encounter in the future* Numerous examples and problems framed using real d...
Autor principal: | |
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Autor Corporativo: | |
Formato: | Libro electrónico |
Idioma: | Inglés |
Publicado: |
Noida :
Pearson India
2015.
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Edición: | 1st ed |
Colección: | Always learning.
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Materias: | |
Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009820525706719 |
Tabla de Contenidos:
- Intro
- Brief Contents
- Contents
- About the Author
- Preface
- I Introduction to Marketing Research
- 1 Marketing Research: An Introduction
- 1.1 Introduction
- 1.2 Difference Between Basic and Applied Research
- 1.3 Defining Marketing Research
- 1.4 Roadmap to Learn Marketing Research
- 1.5 Marketing Research: A Decision Making Tool in the Hands of Management
- 1.6 Use of Software in Data Preparation and Analysis
- 1.7 Ethical Issues in Marketing Research
- Summary 19
- Key Terms
- Discussion Questions
- Case Study
- 2 Marketing Research Process Design
- 2.1 Introduction
- 2.2 Marketing Research Process Design
- Summary 41
- Key Terms
- Discussion Questions
- Case Study
- II Research Design Formulation
- 3 Measurement and Scaling
- 3.1 Introduction
- 3.2 What Should be Measured?
- 3.3 Scales of Measurement
- 3.4 Four Levels of Data Measurement
- 3.5 The Criteria for Good Measurement
- 3.6 Measurement Scales
- 3.7 Factors in Selecting an Appropriate Measurement Scale
- Summary
- Key Terms
- Discussion Questions
- Case Study
- 4 Questionnaire Design
- 4.1 Introduction
- 4.2 What is a Questionnaire?
- 4.3 Questionnaire Design Process
- Summary
- Key Terms
- Discussion Questions
- Case Study
- 5 Sampling and Sampling Distributions
- 5.1 Introduction
- 5.2 Sampling
- 5.3 Why Is Sampling Essential?
- 5.4 The Sampling Design Process
- 5.5 Random Versus Non-Random Sampling
- 5.6 Random Sampling Methods
- 5.7 Non-Random Sampling
- 5.8 Sampling and Non-Sampling Errors
- 5.9 Sampling Distribution
- 5.10 Central Limit Theorem
- 5.11 Sample Distribution of Sample Proportion p
- Summary
- Key Terms
- Discussion Questions
- Numerical Problems
- Case Study
- III Sources and Collection of Data
- 6 Secondary Data Sources
- 6.1 Introduction
- 6.2 Meaning of Primary and Secondary Data.
- 6.3 Benefits and Limitations of Using Secondary Data
- 6.4 Classification of Secondary Data Sources
- 6.5 Roadmap to Use Secondary Data
- Summary
- Key Terms
- Discussion Questions
- Case Study
- 7 Data Collection: Survey and Observation
- 7.1 Introduction
- 7.2 Survey Method of Data Collection
- 7.3 A Classification of Survey Methods
- 7.4 Evaluation Criteria for Survey Methods
- 7.5 Observation Techniques
- 7.6 Classification of Observation Methods
- 7.7 Advantages of Observation Techniques
- 7.8 Limitations of Observation Techniques
- Summary
- Key Terms
- Discussion Questions
- Case Study
- 8 Experimentation
- 8.1 Introduction
- 8.2 Defining Experiments
- 8.3 Some Basic Symbols and Notations in Conducting Experiments
- 8.4 Internal and External Validity in Experimentation
- 8.5 Threats to the Internal Validity of the Experiment
- 8.6 Threats to the External Validity of the Experiment
- 8.7 Ways to Control Extraneous Variables
- 8.8 Laboratory Versus Field Experiment
- 8.9 Experimental Designs and their Classification
- 8.10 Limitations of Experimentation
- 8.11 Test Marketing
- Summary
- Key Terms
- Discussion Questions
- Case Study
- 9 Fieldwork and Data Preparation
- 9.1 Introduction
- 9.2 Fieldwork Process
- 9.3 Data Preparation
- 9.4 Data Preparation Process
- 9.5 Data Analysis
- Summary
- Key Terms
- Discussion Questions
- Case Study
- IV Descriptive Statistics and Data Analysis
- 10 Descriptive Statistics: Measures of Central Tendency
- 10.1 Introduction
- 10.2 Central Tendency
- 10.3 Measures of Central Tendency
- 10.4 Prerequisites for an Ideal Measure of Central Tendency
- 10.5 Mathematical Averages
- 10.6 Positional Averages
- 10.7 Partition Values: Quartiles, Deciles, and Percentiles
- Summary
- Key Terms
- Discussion Questions
- Numerical Problems
- Case Study.
- 11 Descriptive Statistics: Measures of Dispersion
- 11.1 Introduction
- 11.2 Measures of Dispersion
- 11.3 Properties of a Good Measure of Dispersion
- 11.4 Methods of Measuring Dispersion
- 11.5 Empirical Rule
- 11.6 Empirical Relationship Between Measures of Dispersion
- 11.7 Chebyshev's Theorem
- 11.8 Measures of Shape
- 11.9 The Five-Number Summary
- 11.10 Box-and-Whisker Plots
- 11.11 Measures of Association
- Summary
- Key Terms
- Discussion Questions
- Numerical Problems
- Case Study
- 12 Statistical Inference: Hypothesis Testing for Single Populations
- 12.1 Introduction
- 12.2 Introduction to Hypothesis Testing
- 12.3 Hypothesis Testing Procedure
- 12.4 Two-Tailed and One-Tailed Tests of Hypothesis
- 12.5 Type I and Type II Errors
- 12.6 Hypothesis Testing for a Single Population Mean Using the z Statistic
- 12.7 Hypothesis Testing for a Single Population Mean Using the t Statistic (Case of a Small Random Sample When n < 30)
- 12.8 Hypothesis Testing for a Population Proportion
- Summary
- Key Terms
- Discussion Questions
- Numerical Problems
- Case Study
- 13 Statistical Inference: Hypothesis Testing for Two Populations
- 13.1 Introduction
- 13.2 Hypothesis Testing for the Difference Between Two Population Means Using the z Statistic
- 13.3 Hypothesis Testing for the Difference Between Two Population Means Using the t Statistic (Case of a Small Random Sample, n1, n2 < 30, When Population Standard Deviation is Unknown)
- 13.4 Statistical Inference About the Difference Between the Means of Two Related Populations (Matched Samples)
- 13.5 Hypothesis Testing for the Difference in Two Population Proportions
- 13.6 Hypothesis Testing About Two Population Variances (F Distribution)
- Summary
- Key Terms
- Discussion Questions
- Numerical Problems
- Case Study.
- 14 Analysis of Variance and Experimental Designs
- 14.1 Introduction
- 14.2 Introduction to Experimental Designs
- 14.3 Analysis of Variance
- 14.4 Completely Randomized Design (One-Way ANOVA)
- 14.5 Randomized Block Design
- 14.6 Factorial Design (Two-Way ANOVA)
- Summary
- Key Terms
- Discussion Questions
- Numerical Problems
- Case Study
- 15 Hypothesis Testing for Categorical Data (Chi-Square Test)
- 15.1 Introduction
- 15.2 Defining x2-Test Statistic
- 15.3 x2 Goodness-of-Fit Test
- 15.4 x2 Test of Independence: Two-Way Contingency Analysis
- 15.5 x2 Test for Population Variance
- 15.6 x2 Test of Homogeneity
- Summary
- Key Terms
- Discussion Questions
- Numerical Problems
- Case Study
- 16 Correlation and Simple Linear Regression Analysis
- 16.1 Measures of Association
- 16.2 Introduction to Simple Linear Regression
- 16.3 Determining the Equation of a Regression Line
- 16.4 Using MS Excel for Simple Linear Regression
- 16.5 Using Minitab for Simple Linear Regression
- 16.6 Using SPSS for Simple Linear Regression
- 16.7 Measures of Variation
- 16.8 Statistical Inference About Slope, Correlation Coefficient of the Regression Model, and Testing the Overall Model
- Summary
- Key Terms
- Discussion Questions
- Numerical Problems
- Case Study
- 17 Multivariate Analysis I: Multiple Regression Analysis
- 17.1 Introduction
- 17.2 The Multiple Regression Model
- 17.3 Multiple Regression Model with Two Independent Variables
- 17.4 Determination of Coefficient of Multiple Determination (R2), Adjusted R2, and Standard Error of the Estimate
- 17.5 Statistical Significance Test for the Regression Model and the Coefficient of Regression
- 17.6 Indicator (Dummy Variable Model)
- 17.7 Collinearity
- Summary
- Key Terms
- Discussion Questions
- Numerical Problems
- Case Study.
- 18 Multivariate Analysis lI: Discriminant Analysis and Conjoint Analysis
- 18.1 Discriminant Analysis
- 18.2 Conjoint Analysis
- Summary
- Key Terms
- Discussion Questions
- Case Study
- 19 Multivariate Analysis III: Factor Analysis, Cluster Analysis, Multidimensional Scaling and Correspondence Analysis
- 19.1 Factor Analysis
- 19.2 Cluster Analysis
- 19.3 Multidimensional Scaling
- 19.4 Correspondence Analysis
- Summary
- Key Terms
- Discussion Questions
- Case Study
- 20 Sales Forecasting
- 20.1 Introduction
- 20.2 Types of Forecasting Methods
- 20.3 Qualitative Methods of Forecasting
- 20.4 Time Series Analysis
- 20.5 Components of Time Series
- 20.6 Time Series Decomposition Models
- 20.7 The Measurement of Errors in Forecasting
- 20.8 Quantitative Methods of Forecasting
- 20.9 Freehand Method
- 20.10 Smoothing Techniques
- 20.11 Exponential Smoothing Method
- 20.12 Double Exponential Smoothing
- 20.13 Regression Trend Analysis
- 20.14 Seasonal Variation
- 20.15 Solving Problems Involving all Four Components of Time Series
- 20.16 Autocorrelation and Autoregression
- Summary
- Key Terms
- Discussion Questions
- Numerical Problems
- Case Study
- V Result Presentation
- 21 Presentation of Result: Report Writing
- 21.1 Introduction
- 21.2 Organization of the Written Report
- 21.3 Tabular Presentation of Data
- 21.4 Graphical Presentation of Data
- 21.5 Oral Presentation
- Summary
- Key Terms
- Discussion Questions
- Case Study
- VI Applications of Marketing Research
- 22 Marketing Mix Research: Product, Price, Place and Promotion Research
- 22.1 Introduction
- 22.2 Marketing Mix: Meaning
- 22.3 New Product Research
- 22.4 Pricing Research
- 22.5 Distribution (Place) Research
- 22.6 Promotional Research
- Summary
- Key Terms
- Discussion Questions
- Case Study
- Appendix
- Index.