Business Research Method

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 < 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
		11.5.1 Using Minitab for Hypothesis Testing About the Difference in Two Population Proportions
	11.6 Hypothesis Testing About Two Population Variances (F Distribution)
		11.6.1 F Distribution
		11.6.2 Using MS Excel for Hypothesis Testing About Two Population Variances ( F Distribution)
		11.6.3 Using Minitab for 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
		12.7.1 Using SPSS for Post Hoc Comparision
	12.8 Three-Way ANOVA
	12.9 Multivariate Analysis of Variance (MANOVA): A One-way Case
		12.9.1 Using SPSS for MANOVA
	Summary
	Key Terms
	Discussion Questions
	Numerical Problems
	Formulas
	Case 12
13 Hypothesis Testing for Categorical Data (Chi-Square Test)
	13.1 Introduction
	13.2 Defining χ2-test Statistic
		13.2.1 Conditions for Applying the χ2 Test
	13.3 χ2 Goodness-of-Fit Test
		13.3.1 Using MS Excel for Hypothesis Testing with χ2 Statistic for Goodness-of-Fit Test
		13.3.2 Hypothesis Testing for a Population Proportion Using χ2 Goodness-of-Fit Test as an Alternative Technique to the z-Test
	13.4 χ2 Test of Independence: Two-way Contingency Analysis
		13.4.1 Using Minitab for Hypothesis Testing with χ2 Statistic for Test of Independence
	13.5 χ2 Test for Population Variance
	13.6 χ2 Test of Homogeneity 389
	Summary
	Key Terms
	Discussion Questions
	Numerical Problems
	Formulas
	Case 13
14 Non-Parametric Statistics
	14.1 Introduction
	14.2 Runs Test for Randomness of Data
		14.2.1 Small-Sample Runs Test
		14.2.2 Using Minitab for Small-Sample Runs Test
		14.2.3 Using SPSS for Small-Sample Runs Tests
		14.2.4 Large-Sample Runs Test
	14.3 Mann–Whitney U Test
		14.3.1 Small-Sample U Test
		14.3.2 Using Minitab for the Mann–Whitney U Test
		14.3.3 Using Minitab for Ranking
		14.3.4 Using SPSS for the Mann–Whitney U Test
		14.3.5 Using SPSS for Ranking
		14.3.6 U Test for Large Samples
	14.4 Wilcoxon Matched-Pairs Signed Rank Test
		14.4.1 Wilcoxon Test for Small Samples (n ≤ 15)
		14.4.2 Using Minitab for the Wilcoxon Test
		14.4.3 Using SPSS for the Wilcoxon Test
		14.4.4 Wilcoxon Test for Large Samples (n > 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