Business Research Method

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Cover BUSINESS RESEARCH METHODS 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 1.5.1 Problem or Opportunity Identification 1.5.2 Diagnosing the Problem or Opportunity 1.5.3 Executing Business Research to Explore the Solution 1.5.4 Implement Presented Solution 1.5.5 Evaluate the Effectiveness of Decision Making 1.6 Use of Software in Data Preparation and Analysis 1.6.1 Introduction to MS Excel 2007 1.6.2 Introduction to Minitab® 1.6.3 Introduction to SPSS 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 3.3.1 Nominal Scale 3.3.2 Ordinal Scale 3.3.3 Interval Scale 3.3.4 Ratio Scale 3.4 Four Levels of Data Measurement 3.5 The Criteria for Good Measurement 3.5.1 Validity 3.5.2 Reliability 3.5.3 Sensitivity 3.6 Measurement Scales 3.6.1 Single-Item Scales 3.6.2 Multi-Item Scales 3.6.3 Continuous Rating 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 4.1 Introduction 4.2 What is a Questionnaire? 4.3 Questionnaire Design Process 4.3.1 Phase I: Pre-Construction Phase 4.3.2 Phase II: Construction Phase 4.3.3 Phase III: Post-Construction Phase Summary Key Terms Discussion Questions Case 4 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 5.6.1 Simple Random Sampling 5.6.2 Using MS Excel for Random Number Generation 5.6.3 Using Minitab for Random Number Generation 5.6.4 Stratified Random Sampling 5.6.5 Cluster (or Area) Sampling 5.6.6 Systematic (or Quasi-Random) Sampling 5.6.7 Multi-Stage Sampling 5.7 Non-random Sampling 5.7.1 Quota Sampling 5.7.2 Convenience Sampling 5.7.3 Judgement Sampling 5.7.4 Snowball Sampling 5.8 Sampling and Non-Sampling Errors 5.8.1 Sampling Errors 5.8.2 Non-Sampling Errors 5.9 Sampling Distribution 5.10 Central Limit Theorem 5.10.1 Case of Sampling from a Finite Population 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 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 6.4.1 Books, Periodicals, and Other Published Material 6.4.2 Reports and Publication from Government Sources 6.4.3 Computerized Commercial and Other Data Sources 6.4.4 Media Resources 6.5 Roadmap to Use Secondary Data 6.5.1 Step 1: Identifying the Need of Secondary Data for Research 6.5.2 Step 2: Utility of Internal Secondary Data Sources for the Research Problem 6.5.3 Step 3: Utility of External Secondary Data Sources for the Research Problem 6.5.4 Step 4: Use External Secondary Data for the Research Problem Summary Key Terms Discussion Questions Case 6 7 Data Collection: Survey and Observation 7.1 Introduction 7.2 Survey Method of Data Collection 7.3 A Classification of Survey Methods 7.3.1 Personal Interview 7.3.2 Telephone Interview 7.3.3 Mail Interview 7.3.4 Electronic Interview 7.4 Evaluation Criteria for Survey Methods 7.4.1 Cost 7.4.2 Time 7.4.3 Response Rate 7.4.4 Speed of Data Collection 7.4.5 Survey Coverage Area 7.4.6 Bias Due to Interviewer 7.4.7 Quantity of Data 7.4.8 Control Over Fieldwork 7.4.9 Anonymity of the Respondent 7.4.10 Question Posing 7.4.11 Question Diversity 7.5 Observation Techniques 7.5.1 Direct versus Indirect Observation 7.5.2 Structured versus Unstructured Observation 7.5.3 Disguised versus Undisguised Observation 7.5.4 Human versus Mechanical Observation 7.6 Classification of Observation Methods 7.6.1 Personal Observation 7.6.2 Mechanical Observation 7.6.3 Audits 7.6.4 Content Analysis 7.6.5 Physical Trace Analysis 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 8.5.1 History 8.5.2 Maturation 8.5.3 Testing 8.5.4 Instrumentation 8.5.5 Statistical Regression 8.5.6 Selection Bias 8.5.7 Mortality 8.6 Threats to the External Validity of the Experiment 8.6.1 Reactive Effect 8.6.2 Interaction Bias 8.6.3 Multiple Treatment Effect 8.6.4 Non-Representativeness of the Samples 8.7 Ways to Control Extraneous Variables 8.7.1 Randomization 8.7.2 Matching 8.7.3 Statistical Control 8.7.4 Design Control 8.8 Laboratory Versus Field Experiment 8.9 Experimental Designs and their Classification 8.9.1 Pre-Experimental Design 8.9.2 True-Experimental Design 8.9.3 Quasi-Experimental Designs 8.9.4 Statistical Experimental Designs 8.10 Limitations of Experimentation 8.10.1 Time 8.10.2 Cost 8.10.3 Secrecy 8.10.4 Implementation Problems 8.11 Test Marketing 8.11.1 Standard Test Market 8.11.2 Controlled Test Market 8.11.3 Electronic Test Market 8.11.4 Simulated Test Market 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 9.4.1 Preliminary Questionnaire Screening 9.4.2 Editing 9.4.3 Coding 9.4.4 Data Entry 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 10.4.1 Two-Tailed Test of Hypothesis 10.4.2 One-Tailed Test of Hypothesis 10.5 Type I and Type II Errors 10.6 Hypothesis Testing for a Single Population Mean Using the z Statistic 10.6.1 p-Value Approach for Hypothesis Testing 10.6.2 Critical Value Approach for Hypothesis Testing 10.6.3 Using MS Excel for Hypothesis Testing with the z Statistic 10.6.4 Using Minitab for Hypothesis Testing with 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) 10.7.1 Using Minitab for Hypothesis Testing for Single Population Mean Using the t Statistic (Case of a Small Random Sample, n < 30) 10.7.2 Using SPSS for Hypothesis Testing for Single Population Mean Using the t Statistic (Case of a Small Random Sample, n < 30) 10.8 Hypothesis Testing for a Population Proportion 10.8.1 Using Minitab for 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 11.2.1 Using MS Excel for Hypothesis Testing with the z Statistic for the Difference in Means of Two Populations 11.3 Hypothesis Testing for the Difference Between Two Population Means Using the t Statistic (Case of a Small Random Sample, n1, n2 15) 14.5 Kruskal–Wallis Test 14.5.1 Using Minitab for the Kruskal–Wallis Test 14.5.2 Using SPSS for the Kruskal–Wallis Test 14.6 Friedman Test 14.6.1 Using Minitab for the Friedman Test 14.6.2 Using SPSS for the Friedman Test 14.7 Spearman’s Rank Correlation 14.7.1 Using SPSS for Spearman’s Rank Correlation Summary Key Terms Discussion Questions Formulas Numerical Problems Case 14 15 Correlation and Simple Linear Regression Analysis 15.1 Measures of Association 15.1.1 Correlation 15.1.2 Karl Pearson’s Coefficient of Correlation 15.1.3 Using MS Excel for Computing Correlation Coefficient 15.1.4 Using Minitab for Computing Correlation Coefficient 15.1.5 Using SPSS for Computing Correlation Coefficient 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 15.7.1 Coefficient of Determination 15.7.2 Standard Error of the Estimate 15.8 Using Residual Analysis to Test the Assumptions of Regression 15.8.1 Linearity of the Regression Model 15.8.2 Constant Error Variance (Homoscedasticity) 15.8.3 Independence of Error 15.8.4 Normality of Error 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 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 16.4.1 Determination of Coefficient of Multiple Determination (R2) 16.4.2 Adjusted R2 16.4.3 Standard Error of the Estimate 16.5 Residual Analysis for the Multiple Regression Model 16.5.1 Linearity of the Regression Model 16.5.2 Constant Error Variance (Homoscedasticity) 16.5.3 Independence of Error 16.5.4 Normality of Error 16.6 Statistical Significance Test for the Regression Model and the Coefficient of Regression 16.6.1 Testing the Statistical Significance of the Overall Regression Model 16.6.2 t-Test for Testing the Statistical Significance of Regression Coefficients 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 16.9.1 Using MS Excel for the Quadratic Regression Model 16.9.2 Using Minitab for the Quadratic Regression Model 16.9.3 Using SPSS for 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 16.11.1 Testing the Quadratic Effect of a 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 17.1.1 Introduction 17.1.2 Objectives of Discriminant Analysis 17.1.3 Discriminant Analysis Model 17.1.4 Some Statistics Associated with Discriminant Analysis 17.1.5 Steps in Conducting Discriminant Analysis 17.1.6 Using SPSS for Discriminant Analysis 17.1.7 Using Minitab for 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 17.3.1 Steps in Conducting Logistic Regression 17.3.2 Using SPSS for Logistic Regression 17.3.3 Using Minitab for Logistic Regression Summary Key Terms Discussion Questions Case 17 18 Factor Analysis and Cluster Analysis 18.1 Factor Analysis 18.1.1 Introduction 18.1.2 Basic Concept of Using the Factor Analysis 18.1.3 Factor Analysis Model 18.1.4 Some Basic Terms Used in the Factor Analysis 18.1.5 Process of Conducting the Factor Analysis 18.1.6 Using Minitab for the Factor Analysis 18.1.7 Using the SPSS for the 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 19.1 Conjoint Analysis 19.1.1 Introduction 19.1.2 Concept of Performing Conjoint Analysis 19.1.3 Steps in Conducting Conjoint Analysis 19.1.4 Assumptions and Limitations of Conjoint Analysis 19.1.5 Using the SPSS for Conjoint Analysis 19.2 Multidimensional Scaling 19.2.1 Introduction 19.2.2 Some Basic Terms Used in Multidimensional Scaling 19.2.3 The Process of Conducting Multidimensional Scaling 19.2.4 Using SPSS for Multidimensional Scaling 19.3 Correspondence Analysis 19.3.1 Introduction 19.3.2 Process of Conducting Correspondence Analysis 19.3.3 Using SPSS for Correspondence Analysis Summary Key Terms Discussion Questions Case 19 20 Confirmatory Factor Analysis, Structural Equation Modeling and Path Analysis 20.1 Introduction 20.2 Establishing a Difference Between Exploratory Factor Analysis and Confirmatory Factor Analysis 20.2.1 Steps in Conducting Confirmatory Factor Analysis 20.3 Development of Structural Equation Model 20.4 Path Analysis 20.5 Using AMOS for Structural Equation Modeling Summary Key Terms Discussion Questions Case 20 Part V Result Presentation 21 Presentation of Result: Report Writing 21.1 Introduction 21.2 Organization of the Written Report 21.2.1 Title Page 21.2.2 Letter of Transmittal 21.2.3 Letter of Authorization 21.2.4 Table of Contents 21.2.5 Executive Summary 21.2.6 Body 21.2.7 Appendix 21.3 Tabular Presentation of Data 21.4 Graphical Presentation of Data 21.4.1 Bar Chart 21.4.2 Pie Chart 21.4.3 Histogram 21.4.4 Frequency Polygon 21.4.5 Ogive 21.4.6 Scatter Plot 21.5 Oral Presentation Summary Key Terms Discussion Questions Case 21 Appendices Glossary