- Cover
- Title Page
- Dedication
- Contents
- About the Authors
- Preface to the Second Edition
- Preface to the First Edition
- Part I Introduction to Business Research
- 1 Business Research Methods: An Introduction
- 1.1 Introduction
- 1.2 Difference Between Basic and Applied Research
- 1.3 Defining Business Research
- 1.4 Roadmap to Learn Business Research Methods
- 1.5 Business Research Methods: A Decision Making Tool in the Hands of Management
- 1.6 Use of Software in Data Preparation and Analysis
- Summary
- Key Terms
- Discussion Questions
- Case 1
- 2 Business Research Process Design
- 2.1 Introduction
- 2.2 Business Research Process Design
- 2.2.1 Step 1: Problem or Opportunity Identification
- 2.2.2 Step 2: Decision Maker and Business Researcher Meeting to Discuss the Problem or Opportunity Dimensions
- 2.2.3 Step 3: Defining the Management Problem and Subsequently the Research Problem
- 2.2.4 Step 4: Formal Research Proposal and Introducing the Dimensions to the Problem
- 2.2.5 Step 5: Approaches to Research
- 2.2.6 Step 6: Fieldwork and Data Collection
- 2.2.7 Step 7: Data Preparation and Data Entry
- 2.2.8 Step 8: Data Analysis
- 2.2.9 Step 9: Interpretation of Result and Presentation of Findings
- 2.2.10 Step 10: Management Decision and Its Implementation
- Summary
- Key Terms
- Discussion Questions
- Case 2
- Part 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
- 3.7.1 Decision on the Basis of Objective of Conducting a Research
- 3.7.2 Decision Based on the Response Data Type Generated by Using a Scale
- 3.7.3 Decision Based on Using Single- or Multi-Item Scale
- 3.7.4 Decision Based on Forced or Non-Forced Choice
- 3.7.5 Decision Based on Using Balanced or Unbalanced Scale
- 3.7.6 Decision Based on the Number of Scale Points and Its Verbal Description
- Summary
- Key Terms
- Discussion Questions
- Case 3
- Appendix
- 4 Questionnaire Design
- 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 5
- Part III Sources and Collection of Data
- 6 Secondary Data Sources
- 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 7
- 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 8
- 9 Fieldwork and Data Preparation
- 9.1 Introduction
- 9.2 Fieldwork Process
- 9.2.1 Job Analysis, Job Description, and Job Specification
- 9.2.2 Selecting a Fieldworker
- 9.2.3 Providing Training to Fieldworkers
- 9.2.4 Briefing and Sending Fieldworkers to Field for Data Collection
- 9.2.5 Supervising the Fieldwork
- 9.2.6 Debriefing and Fieldwork Validation
- 9.2.7 Evaluating and Terminating the Fieldwork
- 9.3 Data Preparation
- 9.4 Data Preparation Process
- 9.5 Data Analysis
- Summary
- Key Terms
- Discussion Questions
- Case 9
- Part IV Data Analysis and Presentation
- 10 Statistical Inference: Hypothesis Testing for Single Populations
- 10.1 Introduction
- 10.2 Introduction to Hypothesis Testing
- 10.3 Hypothesis Testing Procedure
- 10.4 Two-Tailed and One-Tailed Tests of Hypothesis
- 10.5 Type I and Type II Errors
- 10.6 Hypothesis Testing for a Single Population Mean Using the z Statistic
- 10.7 Hypothesis Testing for a Single Population Mean Using the t Statistic (Case of a Small Random Sample When n < 30)
- 10.8 Hypothesis Testing for a Population Proportion
- Summary
- Key Terms
- Discussion Questions
- Numerical Problems
- Formulas
- Case 10
- 11 Statistical Inference: Hypothesis Testing for Two Populations
- 11.1 Introduction
- 11.2 Hypothesis Testing for the Difference Between Two Population Means Using the z Statistic
- 11.3 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)
- 11.3.1 Using MS Excel for Hypothesis Testing About the Difference Between Two Population Means Using the t Statistic
- 11.3.2 Using Minitab for Hypothesis Testing About the Difference Between Two Population Means Using the t Statistic
- 11.3.3 Using SPSS for Hypothesis Testing About the Difference Between Two Population Means Using the t Statistic
- 11.4 Statistical Inference About the Difference Between the Means of Two Related Populations (Matched Samples)
- 11.4.1 Using MS Excel for Statistical Inference About the Difference Between the Means of Two Related Populations (Matched Samples)
- 11.4.2 Using Minitab for Statistical Inference About the Difference Between the Means of Two Related Populations (Matched Samples)
- 11.4.3 Using SPSS for Statistical Inference About the Difference Between the Means of Two Related Populations (Matched Samples)
- 11.5 Hypothesis Testing for the Difference in Two Population Proportions
- 11.6 Hypothesis Testing About Two Population Variances (F Distribution)
- Summary
- Key Terms
- Discussion Questions
- Numerical Problems
- Formulas
- Case 11
- 12 Analysis of Variance and Experimental Designs
- 12.1 Introduction
- 12.2 Introduction to Experimental Designs
- 12.3 Analysis of Variance
- 12.4 Completely Randomized Design (One-way ANOVA)
- 12.4.1 Steps in Calculating SST (Total Sum of Squares) and Mean Squares in One-Way Analysis of Variance
- 12.4.2 Applying the F-Test Statistic
- 12.4.3 The ANOVA Summary Table
- 12.4.4 Using MS Excel for Hypothesis Testing with the F Statistic for the Difference in Means of More Than Two Populations
- 12.4.5 Using Minitab for Hypothesis Testing with the F Statistic for the Difference in the Means of More Than Two Populations
- 12.4.6 Using SPSS for Hypothesis Testing with the F Statistic for the Difference in Means of More Than Two Populations
- 12.5 Randomized Block Design
- 12.5.1 Null and Alternative Hypotheses in a Randomized Block Design
- 12.5.2 Applying the F-Test Statistic
- 12.5.3 ANOVA Summary Table for Two-Way Classification
- 12.5.4 Using MS Excel for Hypothesis Testing with the F Statistic in a Randomized Block Design
- 12.5.5 Using Minitab for Hypothesis Testing with the F Statistic in a Randomized Block Design
- 12.6 Factorial Design (Two-way ANOVA)
- 12.6.1 Null and Alternative Hypotheses in a Factorial Design
- 12.6.2 Formulas for Calculating SST (Total Sum of Squares) and Mean Squares in a Factorial Design (Two-Way Analysis of Variance)
- 12.6.3 Applying the F-Test Statistic
- 12.6.4 ANOVA Summary Table for Two-Way ANOVA
- 12.6.5 Using MS Excel for Hypothesis Testing with the F Statistic in a Factorial Design
- 12.6.6 Using Minitab for Hypothesis Testing with the F Statistic in a Randomized Block Design
- 12.7 Post Hoc Comparisons in ANOVA
- 12.8 Three-Way ANOVA
- 12.9 Multivariate Analysis of Variance (MANOVA): A One-way Case
- Summary
- Key Terms
- Discussion Questions
- Numerical Problems
- Formulas
- Case 12
- 13 Hypothesis Testing for Categorical Data (Chi-Square Test)
- 14 Non-Parametric Statistics
- 15 Correlation and Simple Linear Regression Analysis
- 15.1 Measures of Association
- 15.2 Introduction to Simple Linear Regression
- 15.3 Determining the Equation of a Regression Line
- 15.4 Using MS Excel for Simple Linear Regression
- 15.5 Using Minitab for Simple Linear Regression
- 15.6 Using SPSS for Simple Linear Regression
- 15.7 Measures of Variation
- 15.8 Using Residual Analysis to Test the Assumptions of Regression
- 15.9 Measuring Autocorrelation: The DurbināWatson Statistic
- 15.10 Statistical Inference About Slope, Correlation Coefficient of the Regression Model, and Testing the Overall Model
- 15.10.1 t Test for the Slope of the Regression Line
- 15.10.2 Testing the Overall Model
- 15.10.3 Estimate of Confidence Interval for the Population Slope ( Ī²1 )
- 15.10.4 Statistical Inference about Correlation Coefficient of the Regression Model
- 15.10.5 Using SPSS for Calculating Statistical Significant Correlation Coefficient for Example 15.2
- 15.10.6 Using Minitab for Calculating Statistical Significant Correlation Coefficient for Example 15.2
- Summary
- Key Terms
- Discussion Questions
- Numerical Problems
- Formulas
- Case 15
- 16 Multiple Regression Analysis
- 16.1 Introduction
- 16.2 The Multiple Regression Model
- 16.3 Multiple Regression Model with Two Independent Variables
- 16.4 Determination of Coefficient of Multiple Determination (R 2), Adjusted R 2, and Standard Error of the Estimate
- 16.5 Residual Analysis for the Multiple Regression Model
- 16.6 Statistical Significance Test for the Regression Model and the Coefficient of Regression
- 16.7 Testing Portions of the Multiple Regression Model
- 16.8 Coefficient of Partial Determination
- 16.9 Non-linear Regression Model: The Quadratic Regression Model
- 16.10 A Case When the Quadratic Regression Model is a Better Alternative to the Simple Regression Model
- 16.11 Testing the Statistical Significance of the Overall Quadratic Regression Model
- 16.12 Indicator (Dummy Variable Model)
- 16.12.1 Using MS Excel for Creating Dummy Variable Column (Assigning 0 and 1 to the Dummy Variable)
- 16.12.2 Using Minitab for Creating Dummy Variable Column (Assigning 0 and 1 to the Dummy Variable)
- 16.12.3 Using SPSS for Creating Dummy Variable Column (Assigning 0 and 1 to the Dummy Variable)
- 16.12.4 Using MS Excel for Interaction
- 16.12.5 Using Minitab for Interaction
- 16.12.6 Using SPSS for Interaction
- 16.13 Model Transformation in Regression Models
- 16.13.1 The Square Root Transformation
- 16.13.2 Using MS Excel for Square Root Transformation
- 16.13.3 Using Minitab for Square Root Transformation
- 16.13.4 Using SPSS for Square Root Transformation
- 16.13.5 Logarithm Transformation
- 16.13.6 Using MS Excel for Log Transformation
- 16.13.7 Using Minitab for Log Transformation
- 16.13.8 Using SPSS for Log Transformation
- 16.14 Collinearity
- 16.15 Model Building
- 16.15.1 Search Procedure
- 16.15.2 All Possible Regressions
- 16.15.3 Stepwise Regression
- 16.15.4 Using Minitab for Stepwise Regression
- 16.15.5 Using SPSS for Stepwise Regression
- 16.15.6 Forward Selection
- 16.15.7 Using Minitab for Forward Selection Regression
- 16.15.8 Using SPSS for Forward Selection Regression
- 16.15.9 Backward Elimination
- 16.15.10 Using Minitab for Backward Elimination Regression
- 16.15.11 Using SPSS for Backward Elimination Regression
- Summary
- Key Terms
- Discussion Questions
- Numerical Problems
- Formulas
- Case 16
- 17 Discriminant Analysis and Logistic Regression Analysis
- 17.1 Discriminant Analysis
- 17.2 Multiple Discriminant Analysis
- 17.2.1 Problem Formulation
- 17.2.2 Computing Discriminant Function Coefficient
- 17.2.3 Testing Statistical Significance of the Discriminant Function
- 17.2.4 Result (Generally Obtained Through Statistical Software) Interpretation
- 17.2.5 Concluding Comment by Performing Classification and Validation of Discriminant Analysis
- 17.3 Logistic (or Logit) Regression Model
- Summary
- Key Terms
- Discussion Questions
- Case 17
- 18 Factor Analysis and Cluster Analysis
- 18.1 Factor Analysis
- 18.2 Cluster Analysis
- 18.2.1 Introduction
- 18.2.2 Basic Concept of Using the Cluster Analysis
- 18.2.3 Some Basic Terms Used in the Cluster Analysis
- 18.2.4 Process of Conducting the Cluster Analysis
- 18.2.5 Non-Hierarchical Clustering
- 18.2.6 Using the SPSS for Hierarchical Cluster Analysis
- 18.2.7 Using the SPSS for Non-Hierarchical Cluster Analysis
- Summary
- Key Terms
- Discussion Questions
- Case 18
- 19 Conjoint Analysis, Multidimensional Scaling and Correspondence Analysis
- 20 Confirmatory Factor Analysis, Structural Equation Modeling and Path Analysis
- Part V Result Presentation
- 21 Presentation of Result: Report Writing
- Appendices
- Glossary
Part IV
Data Analysis and Presentation
Chapter 10 Statistical Inference: Hypothesis testing for Single populations
Chapter 11 Statistical Inference: Hypothesis testing for two Populations
Chapter 12 Analysis of Variance and Experimental Design
Chapter 13 Hypothesis Testing for Categorical Data (Chi-Square Test)
Chapter 14 Non-parametric Statistics
Chapter 15 Correlation and Simple Linear Regression Analysis
Chapter 16 Multiple Regression Analysis
Chapter 17 Discriminant Analysis and Logistic Regression Analysis
Chapter 18 Factor Analysis and Cluster Analysis
Chapter 19 Conjoint Analysis, Multidimensional Scaling and Correspondence Analysis
Chapter 20 Confirmatory Factor Analysis, Structural Equation Modeling and Path Analysis
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