O'Reilly logo

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Business Research Methods, 2nd Edition

Book Description

Business Research Methods, 2e, provides students with the knowledge, understanding and necessary skills to conduct 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 enable students to relate with the context and thus grasp concepts effectively. Keeping in mind the developments in the subject area and necessary feedback from the users of this book, the latest edition has been extensively revised to include the necessary updates. The revision has been carried out in three ways: (i) by adding a few topics in existing chapters, (ii) by restructuring chapters pertaining to multivariate techniques, and (iii) by including a new chapter - Chapter 20: Confirmatory Factor Analysis, Structural Equation Modelling and Path Analysis.

Table of Contents

  1. Cover
  2. Title Page
  3. Dedication
  4. Contents
  5. About the Authors
  6. Preface to the Second Edition
  7. Preface to the First Edition
  8. Part I Introduction to Business Research
  9. 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
  10. 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
  11. Part II Research Design Formulation
  12. 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
    12. Appendix
  13. 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
  14. 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 p
    12. Summary
    13. Key Terms
    14. Discussion Questions
    15. Numerical Problems
    16. Case 5
  15. Part III Sources and Collection of Data
  16. 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
  17. 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
  18. 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
  19. 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
  20. Part IV Data Analysis and Presentation
  21. 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
  22. 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 for Hypothesis Testing About Two Population Variances ( F Distribution)
    7. Summary
    8. Key Terms
    9. Discussion Questions
    10. Numerical Problems
    11. Formulas
    12. Case 11
  23. 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. 12.7 Post Hoc Comparisons in ANOVA
      1. 12.7.1 Using SPSS for Post Hoc Comparision
    8. 12.8 Three-Way ANOVA
    9. 12.9 Multivariate Analysis of Variance (MANOVA): A One-way Case
      1. 12.9.1 Using SPSS for MANOVA
    10. Summary
    11. Key Terms
    12. Discussion Questions
    13. Numerical Problems
    14. Formulas
    15. Case 12
  24. 13 Hypothesis Testing for Categorical Data (Chi-Square Test)
    1. 13.1 Introduction
    2. 13.2 Defining χ2-test Statistic
      1. 13.2.1 Conditions for Applying the χ2 Test
    3. 13.3 χ2 Goodness-of-Fit Test
      1. 13.3.1 Using MS Excel for Hypothesis Testing with χ2 Statistic for Goodness-of-Fit Test
      2. 13.3.2 Hypothesis Testing for a Population Proportion Using χ2 Goodness-of-Fit Test as an Alternative Technique to the z-Test
    4. 13.4 χ2 Test of Independence: Two-way Contingency Analysis
      1. 13.4.1 Using Minitab for Hypothesis Testing with χ2 Statistic for Test of Independence
    5. 13.5 χ2 Test for Population Variance
    6. 13.6 χ2 Test of Homogeneity 389
    7. Summary
    8. Key Terms
    9. Discussion Questions
    10. Numerical Problems
    11. Formulas
    12. Case 13
  25. 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
  26. 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
  27. 16 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 (R 2), Adjusted R 2, 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
  28. 17 Discriminant Analysis and Logistic Regression 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 Logistic (or Logit) Regression Model
      1. 17.3.1 Steps in Conducting Logistic Regression
      2. 17.3.2 Using SPSS for Logistic Regression
      3. 17.3.3 Using Minitab for Logistic Regression
    4. Summary
    5. Key Terms
    6. Discussion Questions
    7. Case 17
  29. 18 Factor Analysis and Cluster 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. Summary
    4. Key Terms
    5. Discussion Questions
    6. Case 18
  30. 19 Conjoint Analysis, Multidimensional Scaling and Correspondence Analysis
    1. 19.1 Conjoint Analysis
      1. 19.1.1 Introduction
      2. 19.1.2 Concept of Performing Conjoint Analysis
      3. 19.1.3 Steps in Conducting Conjoint Analysis
      4. 19.1.4 Assumptions and Limitations of Conjoint Analysis
      5. 19.1.5 Using the SPSS for Conjoint Analysis
    2. 19.2 Multidimensional Scaling
      1. 19.2.1 Introduction
      2. 19.2.2 Some Basic Terms Used in Multidimensional Scaling
      3. 19.2.3 The Process of Conducting Multidimensional Scaling
      4. 19.2.4 Using SPSS for Multidimensional Scaling
    3. 19.3 Correspondence Analysis
      1. 19.3.1 Introduction
      2. 19.3.2 Process of Conducting Correspondence Analysis
      3. 19.3.3 Using SPSS for Correspondence Analysis
    4. Summary
    5. Key Terms
    6. Discussion Questions
    7. Case 19
  31. 20 Confirmatory Factor Analysis, Structural Equation Modeling and Path Analysis
    1. 20.1 Introduction
    2. 20.2 Establishing a Difference Between Exploratory Factor Analysis and Confirmatory Factor Analysis
      1. 20.2.1 Steps in Conducting Confirmatory Factor Analysis
    3. 20.3 Development of Structural Equation Model
    4. 20.4 Path Analysis
    5. 20.5 Using AMOS for Structural Equation Modeling
    6. Summary
    7. Key Terms
    8. Discussion Questions
    9. Case 20
  32. Part V Result Presentation
  33. 21 Presentation of Result: Report Writing
    1. 21.1 Introduction
    2. 21.2 Organization of the Written Report
      1. 21.2.1 Title Page
      2. 21.2.2 Letter of Transmittal
      3. 21.2.3 Letter of Authorization
      4. 21.2.4 Table of Contents
      5. 21.2.5 Executive Summary
      6. 21.2.6 Body
      7. 21.2.7 Appendix
    3. 21.3 Tabular Presentation of Data
    4. 21.4 Graphical Presentation of Data
      1. 21.4.1 Bar Chart
      2. 21.4.2 Pie Chart
      3. 21.4.3 Histogram
      4. 21.4.4 Frequency Polygon
      5. 21.4.5 Ogive
      6. 21.4.6 Scatter Plot
    5. 21.5 Oral Presentation
    6. Summary
    7. Key Terms
    8. Discussion Questions
    9. Case 21
  34. Appendices
  35. Glossary