Book description
Business Research Methods provides students with the knowledge, understanding and necessary skills to complete a business research. The reader is taken step-by-step through a range of contemporary research methods, while numerous worked examples and real-life case studies bring to life the realities of undertaking these researchs. Emphasis on data analysis is the key strength of this book. The book uses the latest software packages: MS Excel (2007), SPSS 17 and Minitab 15 to solve statistical data analysis. The complexity of multivariate analysis is also dealt with the help of these three softwares.
Table of contents
- Cover
- Title Page
- Brief Contents
- Contents
- About the Author
- Dedication
- Preface
-
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
-
1. Business Research Methods: An Introduction
-
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
- 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 p̅
- Summary
- Key Terms
- Discussion Questions
- Numerical Problems
- Case 5
-
3. Measurement and Scaling
-
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
-
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
- 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 Durbin–Watson 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 (β1)
- 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. Multivariate Analysis—I: 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 (R2), Adjusted R2, 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. Multivariate Analysis—II: Discriminant Analysis and Conjoint 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 Conjoint Analysis
- Summary
- Key Terms
- Discussion Questions
- Case 17
-
18. Multivariate Analysis—III: Factor Analysis, Cluster Analysis, Multidimensional Scaling, and Correspondence 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
- 18.3 Multidimensional Scaling
- 18.4 Correspondence Analysis
- Summary
- Key Terms
- Discussion Questions
- Case 18
-
10. Statistical Inference: Hypothesis Testing for Single Populations
- V Result Presentation
- Appendices
- Glossary
- Copyright
Product information
- Title: Business Research Methods
- Author(s):
- Release date: March 2011
- Publisher(s): Pearson India
- ISBN: 9788131754481
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