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Business Research Methods

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

  1. Cover
  2. Title Page
  3. Brief Contents
  4. Contents
  5. About the Author
  6. Dedication
  7. Preface
  8. I Introduction to Business Research
    1. 1. Business Research Methods: An Introduction
      1. 1.1 Introduction
      2. 1.2 Difference Between Basic and Applied Research
      3. 1.3 Defining Business Research
      4. 1.4 Roadmap to Learn Business Research Methods
      5. 1.5 Business Research Methods: A Decision Making Tool in the Hands of Management
        1. 1.5.1 Problem or Opportunity Identification
        2. 1.5.2 Diagnosing the Problem or Opportunity
        3. 1.5.3 Executing Business Research to Explore the Solution
        4. 1.5.4 Implement Presented Solution
        5. 1.5.5 Evaluate the Effectiveness of Decision Making
      6. 1.6 Use of Software in Data Preparation and Analysis
        1. 1.6.1 Introduction to MS Excel 2007
        2. 1.6.2 Introduction to Minitab®
        3. 1.6.3 Introduction to SPSS
      7. Summary
      8. Key Terms
      9. Discussion Questions
      10. Case 1
    2. 2. Business Research Process Design
      1. 2.1 Introduction
      2. 2.2 Business Research Process Design
        1. 2.2.1 Step 1: Problem or Opportunity Identification
        2. 2.2.2 Step 2: Decision Maker and Business Researcher Meeting to Discuss the Problem or Opportunity Dimensions
        3. 2.2.3 Step 3: Defining the Management Problem and Subsequently the Research Problem
        4. 2.2.4 Step 4: Formal Research Proposal and Introducing the Dimensions to the Problem
        5. 2.2.5 Step 5: Approaches to Research
        6. 2.2.6 Step 6: Fieldwork and Data Collection
        7. 2.2.7 Step 7: Data Preparation and Data Entry
        8. 2.2.8 Step 8: Data Analysis
        9. 2.2.9 Step 9: Interpretation of Result and Presentation of Findings
        10. 2.2.10 Step 10: Management Decision and Its Implementation
      3. Summary
      4. Key Terms
      5. Discussion Questions
      6. Case 2
  9. II Research Design Formulation
    1. 3. Measurement and Scaling
      1. 3.1 Introduction
      2. 3.2 What Should be Measured?
      3. 3.3 Scales of Measurement
        1. 3.3.1 Nominal Scale
        2. 3.3.2 Ordinal Scale
        3. 3.3.3 Interval Scale
        4. 3.3.4 Ratio Scale
      4. 3.4 Four Levels of Data Measurement
      5. 3.5 The Criteria for Good Measurement
        1. 3.5.1 Validity
        2. 3.5.2 Reliability
        3. 3.5.3 Sensitivity
      6. 3.6 Measurement Scales
        1. 3.6.1 Single-Item Scales
        2. 3.6.2 Multi-Item Scales
        3. 3.6.3 Continuous Rating Scales
      7. 3.7 Factors in Selecting an Appropriate Measurement Scale
        1. 3.7.1 Decision on the Basis of Objective of Conducting a Research
        2. 3.7.2 Decision Based on the Response Data Type Generated by Using a Scale
        3. 3.7.3 Decision Based on Using Single- or Multi-Item Scale
        4. 3.7.4 Decision Based on Forced or Non-Forced Choice
        5. 3.7.5 Decision Based on Using Balanced or Unbalanced Scale
        6. 3.7.6 Decision Based on the Number of Scale Points and Its Verbal Description
      8. Summary
      9. Key Terms
      10. Discussion Questions
      11. Case 3
    2. 4. Questionnaire Design
      1. 4.1 Introduction
      2. 4.2 What is a Questionnaire?
      3. 4.3 Questionnaire Design Process
        1. 4.3.1 Phase I: Pre-Construction Phase
        2. 4.3.2 Phase II: Construction Phase
        3. 4.3.3 Phase III: Post-Construction Phase
      4. Summary
      5. Key Terms
      6. Discussion Questions
      7. Case 4
    3. 5. Sampling and Sampling Distributions
      1. 5.1 Introduction
      2. 5.2 Sampling
      3. 5.3 Why Is Sampling Essential?
      4. 5.4 The Sampling Design Process
      5. 5.5 Random versus Non-Random Sampling
      6. 5.6 Random Sampling Methods
        1. 5.6.1 Simple Random Sampling
        2. 5.6.2 Using MS Excel for Random Number Generation
        3. 5.6.3 Using Minitab for Random Number Generation
        4. 5.6.4 Stratified Random Sampling
        5. 5.6.5 Cluster (or Area) Sampling
        6. 5.6.6 Systematic (or Quasi-Random) Sampling
        7. 5.6.7 Multi-Stage Sampling
      7. 5.7 Non-random Sampling
        1. 5.7.1 Quota Sampling
        2. 5.7.2 Convenience Sampling
        3. 5.7.3 Judgement Sampling
        4. 5.7.4 Snowball Sampling
      8. 5.8 Sampling and Non-Sampling Errors
        1. 5.8.1 Sampling Errors
        2. 5.8.2 Non-Sampling Errors
      9. 5.9 Sampling Distribution
      10. 5.10 Central Limit Theorem
        1. 5.10.1 Case of Sampling from a Finite Population
      11. 5.11 Sample Distribution of Sample Proportion p̅
      12. Summary
      13. Key Terms
      14. Discussion Questions
      15. Numerical Problems
      16. Case 5
  10. III Sources and Collection of Data
    1. 6. Secondary Data Sources
      1. 6.1 Introduction
      2. 6.2 Meaning of Primary and Secondary Data
      3. 6.3 Benefits and Limitations of Using Secondary Data
      4. 6.4 Classification of Secondary Data Sources
        1. 6.4.1 Books, Periodicals, and Other Published Material
        2. 6.4.2 Reports and Publication from Government Sources
        3. 6.4.3 Computerized Commercial and Other Data Sources
        4. 6.4.4 Media Resources
      5. 6.5 Roadmap to Use Secondary Data
        1. 6.5.1 Step 1: Identifying the Need of Secondary Data for Research
        2. 6.5.2 Step 2: Utility of Internal Secondary Data Sources for the Research Problem
        3. 6.5.3 Step 3: Utility of External Secondary Data Sources for the Research Problem
        4. 6.5.4 Step 4: Use External Secondary Data for the Research Problem
      6. Summary
      7. Key Terms
      8. Discussion Questions
      9. Case 6
    2. 7. Data Collection: Survey and Observation
      1. 7.1 Introduction
      2. 7.2 Survey Method of Data Collection
      3. 7.3 A Classification of Survey Methods
        1. 7.3.1 Personal Interview
        2. 7.3.2 Telephone Interview
        3. 7.3.3 Mail Interview
        4. 7.3.4 Electronic Interview
      4. 7.4 Evaluation Criteria for Survey Methods
        1. 7.4.1 Cost
        2. 7.4.2 Time
        3. 7.4.3 Response Rate
        4. 7.4.4 Speed of Data Collection
        5. 7.4.5 Survey Coverage Area
        6. 7.4.6 Bias Due to Interviewer
        7. 7.4.7 Quantity of Data
        8. 7.4.8 Control Over Fieldwork
        9. 7.4.9 Anonymity of the Respondent
        10. 7.4.10 Question Posing
        11. 7.4.11 Question Diversity
      5. 7.5 Observation Techniques
        1. 7.5.1 Direct versus Indirect Observation
        2. 7.5.2 Structured versus Unstructured Observation
        3. 7.5.3 Disguised versus Undisguised Observation
        4. 7.5.4 Human versus Mechanical Observation
      6. 7.6 Classification of Observation Methods
        1. 7.6.1 Personal Observation
        2. 7.6.2 Mechanical Observation
        3. 7.6.3 Audits
        4. 7.6.4 Content Analysis
        5. 7.6.5 Physical Trace Analysis
      7. 7.7 Advantages of Observation Techniques
      8. 7.8 Limitations of Observation Techniques
      9. Summary
      10. Key Terms
      11. Discussion Questions
      12. Case 7
    3. 8. Experimentation
      1. 8.1 Introduction
      2. 8.2 Defining Experiments
      3. 8.3 Some Basic Symbols and Notations in Conducting Experiments
      4. 8.4 Internal and External Validity in Experimentation
      5. 8.5 Threats to the Internal Validity of the Experiment
        1. 8.5.1 History
        2. 8.5.2 Maturation
        3. 8.5.3 Testing
        4. 8.5.4 Instrumentation
        5. 8.5.5 Statistical Regression
        6. 8.5.6 Selection Bias
        7. 8.5.7 Mortality
      6. 8.6 Threats to the External Validity of the Experiment
        1. 8.6.1 Reactive Effect
        2. 8.6.2 Interaction Bias
        3. 8.6.3 Multiple Treatment Effect
        4. 8.6.4 Non-Representativeness of the Samples
      7. 8.7 Ways to Control Extraneous Variables
        1. 8.7.1 Randomization
        2. 8.7.2 Matching
        3. 8.7.3 Statistical Control
        4. 8.7.4 Design Control
      8. 8.8 Laboratory versus Field Experiment
      9. 8.9 Experimental Designs and Their Classification
        1. 8.9.1 Pre-Experimental Design
        2. 8.9.2 True-Experimental Design
        3. 8.9.3 Quasi-Experimental Designs
        4. 8.9.4 Statistical Experimental Designs
      10. 8.10 Limitations of Experimentation
        1. 8.10.1 Time
        2. 8.10.2 Cost
        3. 8.10.3 Secrecy
        4. 8.10.4 Implementation Problems
      11. 8.11 Test Marketing
        1. 8.11.1 Standard Test Market
        2. 8.11.2 Controlled Test Market
        3. 8.11.3 Electronic Test Market
        4. 8.11.4 Simulated Test Market
      12. Summary
      13. Key Terms
      14. Discussion Questions
      15. Case 8
    4. 9. Fieldwork and Data Preparation
      1. 9.1 Introduction
      2. 9.2 Fieldwork Process
        1. 9.2.1 Job Analysis, Job Description, and Job Specification
        2. 9.2.2 Selecting a Fieldworker
        3. 9.2.3 Providing Training to Fieldworkers
        4. 9.2.4 Briefing and Sending Fieldworkers to Field for Data Collection
        5. 9.2.5 Supervising the Fieldwork
        6. 9.2.6 Debriefing and Fieldwork Validation
        7. 9.2.7 Evaluating and Terminating the Fieldwork
      3. 9.3 Data Preparation
      4. 9.4 Data Preparation Process
        1. 9.4.1 Preliminary Questionnaire Screening
        2. 9.4.2 Editing
        3. 9.4.3 Coding
        4. 9.4.4 Data Entry
      5. 9.5 Data Analysis
      6. Summary
      7. Key Terms
      8. Discussion Questions
      9. Case 9
  11. IV Data Analysis and Presentation
    1. 10. Statistical Inference: Hypothesis Testing for Single Populations
      1. 10.1 Introduction
      2. 10.2 Introduction to Hypothesis Testing
      3. 10.3 Hypothesis Testing Procedure
      4. 10.4 Two-Tailed and One-Tailed Tests of Hypothesis
        1. 10.4.1 Two-Tailed Test of Hypothesis
        2. 10.4.2 One-Tailed Test of Hypothesis
      5. 10.5 Type I and Type II Errors
      6. 10.6 Hypothesis Testing for a Single Population Mean Using the z Statistic
        1. 10.6.1 p-Value Approach for Hypothesis Testing
        2. 10.6.2 Critical Value Approach for Hypothesis Testing
        3. 10.6.3 Using MS Excel for Hypothesis Testing with the z Statistic
        4. 10.6.4 Using Minitab for Hypothesis Testing with the z Statistic
      7. 10.7 Hypothesis Testing for a Single Population Mean Using the t Statistic (Case of a Small Random Sample when n <30)
        1. 10.7.1 Using Minitab for Hypothesis Testing for Single Population Mean Using the t Statistic (Case of a Small Random Sample, n <30)
        2. 10.7.2 Using SPSS for Hypothesis Testing for Single Population Mean Using the t Statistic (Case of a Small Random Sample, n <30)
      8. 10.8 Hypothesis Testing for a Population Proportion
        1. 10.8.1 Using Minitab for Hypothesis Testing for a Population Proportion
      9. Summary
      10. Key Terms
      11. Discussion Questions
      12. Numerical Problems
      13. Formulas
      14. Case 10
    2. 11. Statistical Inference: Hypothesis Testing for Two Populations
      1. 11.1 Introduction
      2. 11.2 Hypothesis Testing for the Difference Between Two Population Means Using the z Statistic
        1. 11.2.1 Using MS Excel for Hypothesis Testing with the z Statistic for the Difference in Means of Two Populations
      3. 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)
        1. 11.3.1 Using MS Excel for Hypothesis Testing About the Difference Between Two Population Means Using the t Statistic
        2. 11.3.2 Using Minitab for Hypothesis Testing About the Difference Between Two Population Means Using the t Statistic
        3. 11.3.3 Using SPSS for Hypothesis Testing About the Difference Between Two Population Means Using the t Statistic
      4. 11.4 Statistical Inference About the Difference Between the Means of Two Related Populations (Matched Samples)
        1. 11.4.1 Using MS Excel for Statistical Inference About the Difference Between the Means of Two Related Populations (Matched Samples)
        2. 11.4.2 Using Minitab for Statistical Inference About the Difference Between the Means of Two Related Populations (Matched Samples)
        3. 11.4.3 Using SPSS for Statistical Inference About the Difference Between the Means of Two Related Populations (Matched Samples)
      5. 11.5 Hypothesis Testing for the Difference in Two Population Proportions
        1. 11.5.1 Using Minitab for Hypothesis Testing About the Difference in Two Population Proportions
      6. 11.6 Hypothesis Testing About Two Population Variances (F Distribution)
        1. 11.6.1 F Distribution
        2. 11.6.2 Using MS Excel for Hypothesis Testing About Two Population Variances (F Distribution)
        3. 11.6.3 Using Minitab r Hypothesis Testing About Two Population Variances (F Distribution)
      7. Summary
      8. Key Terms
      9. Discussion Questions
      10. Numerical Problems
      11. Formulas
      12. Case 11
    3. 12. Analysis of Variance and Experimental Designs
      1. 12.1 Introduction
      2. 12.2 Introduction to Experimental Designs
      3. 12.3 Analysis of Variance
      4. 12.4 Completely Randomized Design (One-Way ANOVA)
        1. 12.4.1 Steps in Calculating SST (Total Sum of Squares) and Mean Squares in One-Way Analysis of Variance
        2. 12.4.2 Applying the F-Test Statistic
        3. 12.4.3 The ANOVA Summary Table
        4. 12.4.4 Using MS Excel for Hypothesis Testing with the F Statistic for the Difference in Means of More Than Two Populations
        5. 12.4.5 Using Minitab for Hypothesis Testing with the F Statistic for the Difference in the Means of More Than Two Populations
        6. 12.4.6 Using SPSS for Hypothesis Testing with the F Statistic for the Difference in Means of More Than Two Populations
      5. 12.5 Randomized Block Design
        1. 12.5.1 Null and Alternative Hypotheses in a Randomized Block Design
        2. 12.5.2 Applying the F-Test Statistic
        3. 12.5.3 ANOVA Summary Table for Two-Way Classification
        4. 12.5.4 Using MS Excel for Hypothesis Testing with the F Statistic in a Randomized Block Design
        5. 12.5.5 Using Minitab for Hypothesis Testing with the F Statistic in a Randomized Block Design
      6. 12.6 Factorial Design (Two-Way ANOVA)
        1. 12.6.1 Null and Alternative Hypotheses in a Factorial Design
        2. 12.6.2 Formulas for Calculating SST (Total Sum of Squares) and Mean Squares in a Factorial Design (Two-Way Analysis of Variance)
        3. 12.6.3 Applying the F-Test Statistic
        4. 12.6.4 ANOVA Summary Table for Two-Way ANOVA
        5. 12.6.5 Using MS Excel for Hypothesis Testing with the F Statistic in a Factorial Design
        6. 12.6.6 Using Minitab for Hypothesis Testing with the F Statistic in a Randomized Block Design
      7. Summary
      8. Key Terms
      9. Discussion Questions
      10. Numerical Problems
      11. Formulas
      12. Case 12
    4. 13. Hypothesis Testing for Categorical Data (Chi-Square Test)
      1. 13.1 Introduction
      2. 13.2 Defining x2-Test Statistic
        1. 13.2.1 Conditions for Applying the x2 Test
      3. 13.3 x2 Goodness-of-Fit Test
        1. 13.3.1 Using MS Excel for Hypothesis Testing with x2 Statistic for Goodness-of-Fit Test
        2. 13.3.2 Hypothesis Testing for a Population Proportion Using x2 Goodness-of-Fit Test as an Alternative Technique to the z-Test
      4. 13.4 x2 Test of Independence: Two-Way Contingency Analysis
        1. 13.4.1 Using Minitab for Hypothesis Testing with x2 Statistic for Test of Independence
      5. 13.5 x2 Test for Population Variance
      6. 13.6 x2 Test of Homogeneity
      7. Summary
      8. Key Terms
      9. Discussion Questions
      10. Numerical Problems
      11. Formulas
      12. Case 13
    5. 14. Non-Parametric Statistics
      1. 14.1 Introduction
      2. 14.2 Runs Test for Randomness of Data
        1. 14.2.1 Small-Sample Runs Test
        2. 14.2.2 Using Minitab for Small-Sample Runs Test
        3. 14.2.3 Using SPSS for Small-Sample Runs Tests
        4. 14.2.4 Large-Sample Runs Test
      3. 14.3 Mann–Whitney U Test
        1. 14.3.1 Small-Sample U Test
        2. 14.3.2 Using Minitab for the Mann–Whitney U Test
        3. 14.3.3 Using Minitab for Ranking
        4. 14.3.4 Using SPSS for the Mann–Whitney U Test
        5. 14.3.5 Using SPSS for Ranking
        6. 14.3.6 U Test for Large Samples
      4. 14.4 Wilcoxon Matched-Pairs Signed Rank Test
        1. 14.4.1 Wilcoxon Test for Small Samples (n ≤15)
        2. 14.4.2 Using Minitab for the Wilcoxon Test
        3. 14.4.3 Using SPSS for the Wilcoxon Test
        4. 14.4.4 Wilcoxon Test for Large Samples (n >15)
      5. 14.5 Kruskal–Wallis Test
        1. 14.5.1 Using Minitab for the Kruskal–Wallis Test
        2. 14.5.2 Using SPSS for the Kruskal–Wallis Test
      6. 14.6 Friedman Test
        1. 14.6.1 Using Minitab for the Friedman Test
        2. 14.6.2 Using SPSS for the Friedman Test
      7. 14.7 Spearman’s Rank Correlation
        1. 14.7.1 Using SPSS for Spearman’s Rank Correlation
      8. Summary
      9. Key Terms
      10. Discussion Questions
      11. Formulas
      12. Numerical Problems
      13. Case 14
    6. 15. Correlation and Simple Linear Regression Analysis
      1. 15.1 Measures of Association
        1. 15.1.1 Correlation
        2. 15.1.2 Karl Pearson’s Coefficient of Correlation
        3. 15.1.3 Using MS Excel for Computing Correlation Coefficient
        4. 15.1.4 Using Minitab for Computing Correlation Coefficient
        5. 15.1.5 Using SPSS for Computing Correlation Coefficient
      2. 15.2 Introduction to Simple Linear Regression
      3. 15.3 Determining the Equation of a Regression Line
      4. 15.4 Using MS Excel for Simple Linear Regression
      5. 15.5 Using Minitab for Simple Linear Regression
      6. 15.6 Using SPSS for Simple Linear Regression
      7. 15.7 Measures of Variation
        1. 15.7.1 Coefficient of Determination
        2. 15.7.2 Standard Error of the Estimate
      8. 15.8 Using Residual Analysis to Test the Assumptions of Regression
        1. 15.8.1 Linearity of the Regression Model
        2. 15.8.2 Constant Error Variance (Homoscedasticity)
        3. 15.8.3 Independence of Error
        4. 15.8.4 Normality of Error
      9. 15.9 Measuring Autocorrelation: The Durbin–Watson Statistic
      10. 15.10 Statistical Inference About Slope, Correlation Coefficient of the Regression Model, and Testing the Overall Model
        1. 15.10.1 t Test for the Slope of the Regression Line
        2. 15.10.2 Testing the Overall Model
        3. 15.10.3 Estimate of Confidence Interval for the Population Slope (β1)
        4. 15.10.4 Statistical Inference about Correlation Coefficient of the Regression Model
        5. 15.10.5 Using SPSS for Calculating Statistical Significant Correlation Coefficient for Example 15.2
        6. 15.10.6 Using Minitab for Calculating Statistical Significant Correlation Coefficient for Example 15.2
      11. Summary
      12. Key Terms
      13. Discussion Questions
      14. Numerical Problems
      15. Formulas
      16. Case 15
    7. 16. Multivariate Analysis—I: Multiple Regression Analysis
      1. 16.1 Introduction
      2. 16.2 The Multiple Regression Model
      3. 16.3 Multiple Regression Model with Two Independent Variables
      4. 16.4 Determination of Coefficient of Multiple Determination (R2), Adjusted R2, and Standard Error of the Estimate
        1. 16.4.1 Determination of Coefficient of Multiple Determination (R2)
        2. 16.4.2 Adjusted R2
        3. 16.4.3 Standard Error of the Estimate
      5. 16.5 Residual Analysis for the Multiple Regression Model
        1. 16.5.1 Linearity of the Regression Model
        2. 16.5.2 Constant Error Variance (Homoscedasticity)
        3. 16.5.3 Independence of Error
        4. 16.5.4 Normality of Error
      6. 16.6 Statistical Significance Test for the Regression Model and the Coefficient of Regression
        1. 16.6.1 Testing the Statistical Significance of the Overall Regression Model
        2. 16.6.2 t Test for Testing the Statistical Significance of Regression Coefficients
      7. 16.7 Testing Portions of the Multiple Regression Model
      8. 16.8 Coefficient of Partial Determination
      9. 16.9 Non-Linear Regression Model: The Quadratic Regression Model
        1. 16.9.1 Using MS Excel for the Quadratic Regression Model
        2. 16.9.2 Using Minitab for the Quadratic Regression Model
        3. 16.9.3 Using SPSS for the Quadratic Regression Model
      10. 16.10 A Case when the Quadratic Regression Model is a Better Alternative to the Simple Regression Model
      11. 16.11 Testing the Statistical Significance of the Overall Quadratic Regression Model
        1. 16.11.1 Testing the Quadratic Effect of a Quadratic Regression Model
      12. 16.12 Indicator (Dummy Variable Model)
        1. 16.12.1 Using MS Excel for Creating Dummy Variable Column (Assigning 0 and 1 to the Dummy Variable)
        2. 16.12.2 Using Minitab for Creating Dummy Variable Column (Assigning 0 and 1 to the Dummy Variable)
        3. 16.12.3 Using SPSS for Creating Dummy Variable Column (Assigning 0 and 1 to the Dummy Variable)
        4. 16.12.4 Using MS Excel for Interaction
        5. 16.12.5 Using Minitab for Interaction
        6. 16.12.6 Using SPSS for Interaction
      13. 16.13 Model Transformation in Regression Models
        1. 16.13.1 The Square Root Transformation
        2. 16.13.2 Using MS Excel for Square Root Transformation
        3. 16.13.3 Using Minitab for Square Root Transformation
        4. 16.13.4 Using SPSS for Square Root Transformation
        5. 16.13.5 Logarithm Transformation
        6. 16.13.6 Using MS Excel for Log Transformation
        7. 16.13.7 Using Minitab for Log Transformation
        8. 16.13.8 Using SPSS for Log Transformation
      14. 16.14 Collinearity
      15. 16.15 Model Building
        1. 16.15.1 Search Procedure
        2. 16.15.2 All Possible Regressions
        3. 16.15.3 Stepwise Regression
        4. 16.15.4 Using Minitab for Stepwise Regression
        5. 16.15.5 Using SPSS for Stepwise Regression
        6. 16.15.6 Forward Selection
        7. 16.15.7 Using Minitab for Forward Selection Regression
        8. 16.15.8 Using SPSS for Forward Selection Regression
        9. 16.15.9 Backward Elimination
        10. 16.15.10 Using Minitab for Backward Elimination Regression
        11. 16.15.11 Using SPSS for Backward Elimination Regression
      16. Summary
      17. Key Terms
      18. Discussion Questions
      19. Numerical Problems
      20. Formulas
      21. Case 16
    8. 17. Multivariate Analysis—II: Discriminant Analysis and Conjoint Analysis
      1. 17.1 Discriminant Analysis
        1. 17.1.1 Introduction
        2. 17.1.2 Objectives of Discriminant Analysis
        3. 17.1.3 Discriminant Analysis Model
        4. 17.1.4 Some Statistics Associated with Discriminant Analysis
        5. 17.1.5 Steps in Conducting Discriminant Analysis
        6. 17.1.6 Using SPSS for Discriminant Analysis
        7. 17.1.7 Using Minitab for Discriminant Analysis
      2. 17.2 Multiple Discriminant Analysis
        1. 17.2.1 Problem Formulation
        2. 17.2.2 Computing Discriminant Function Coefficient
        3. 17.2.3 Testing Statistical Significance of the Discriminant Function
        4. 17.2.4 Result (Generally Obtained Through Statistical Software) Interpretation
        5. 17.2.5 Concluding Comment by Performing Classification and Validation of Discriminant Analysis
      3. 17.3 Conjoint Analysis
        1. 17.3.1 Introduction
        2. 17.3.2 Concept of Performing Conjoint Analysis
        3. 17.3.3 Steps in Conducting Conjoint Analysis
        4. 17.3.4 Assumptions and Limitations of Conjoint Analysis
      4. Summary
      5. Key Terms
      6. Discussion Questions
      7. Case 17
    9. 18. Multivariate Analysis—III: Factor Analysis, Cluster Analysis, Multidimensional Scaling, and Correspondence Analysis
      1. 18.1 Factor Analysis
        1. 18.1.1 Introduction
        2. 18.1.2 Basic Concept of Using the Factor Analysis
        3. 18.1.3 Factor Analysis Model
        4. 18.1.4 Some Basic Terms Used in the Factor Analysis
        5. 18.1.5 Process of Conducting the Factor Analysis
        6. 18.1.6 Using Minitab for the Factor Analysis
        7. 18.1.7 Using the SPSS for the Factor Analysis
      2. 18.2 Cluster Analysis
        1. 18.2.1 Introduction
        2. 18.2.2 Basic Concept of Using the Cluster Analysis
        3. 18.2.3 Some Basic Terms Used in the Cluster Analysis
        4. 18.2.4 Process of Conducting the Cluster Analysis
        5. 18.2.5 Non-Hierarchical Clustering
        6. 18.2.6 Using the SPSS for Hierarchical Cluster Analysis
        7. 18.2.7 Using the SPSS for Non-Hierarchical Cluster Analysis
      3. 18.3 Multidimensional Scaling
        1. 18.3.1 Introduction
        2. 18.3.2 Some Basic Terms Used in Multidimensional Scaling
        3. 18.3.3 The Process of Conducting Multidimensional Scaling
        4. 18.3.4 Using SPSS for Multidimensional Scaling
      4. 18.4 Correspondence Analysis
      5. Summary
      6. Key Terms
      7. Discussion Questions
      8. Case 18
  12. V Result Presentation
    1. 19. Presentation of Result: Report Writing
      1. 19.1 Introduction
      2. 19.2 Organization of the Written Report
        1. 19.2.1 Title Page
        2. 19.2.2 Letter of Transmittal
        3. 19.2.3 Letter of Authorization
        4. 19.2.4 Table of Contents
        5. 19.2.5 Executive Summary
        6. 19.2.6 Body
        7. 19.2.7 Appendix
      3. 19.3 Tabular Presentation of Data
      4. 19.4 Graphical Presentation of Data
        1. 19.4.1 Bar Chart
        2. 19.4.2 Pie Chart
        3. 19.4.3 Histogram
        4. 19.4.4 Frequency Polygon
        5. 19.4.5 Ogive
        6. 19.4.6 Scatter Plot
      5. 19.5 Oral Presentation
      6. Summary
      7. Key Terms
      8. Discussion Questions
      9. Case 19
  13. Appendices
  14. Glossary
  15. Copyright