Business Research Methods

Cover
Brief Contents
Contents
About the Author
Preface
Part I: Introduction to Business Research
	Chapter 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
		References
		Summary
		Key Terms
		Notes
		Discussion Questions
		Case Study
		Notes
	Chapter 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
		References
		Summary
		Key Terms
		Notes
		Discussion Questions
		Case Study
		Notes
Part II: Research Design Formulation
	Chapter 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
		References
		Summary
		Key Terms
		Notes
		Discussion Questions
		Case Study
		Notes
	Chapter 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
		References
		Summary
		Note
		Key Terms
		Discussion Questions
		Case Study
		Notes
	Chapter 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
		Notes
		Discussion Questions
		Numerical Problems
		Case Study
		Notes
Part III: Sources and Collection of Data
	Chapter 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
		References
		Summary
		Key Terms
		Notes
		Discussion Questions
		Case Study
		Notes
	Chapter 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
		References
		Summary
		Key Terms
		Notes
		Discussion Questions
		Case Study
		Notes
	Chapter 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
		References
		Summary
		Key Terms
		Notes
		Discussion Questions
		Case Study
		Notes
	Chapter 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 Field Workers 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
		References
		Summary
		Key Terms
		Notes
		Discussion Questions
		Case Study
		Notes
Part IV: Data Analysis and Presentation
	Chapter 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
		Notes
		Discussion Questions
		Numerical Problems
		Formulas
		Case Study
		Notes
	Chapter 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 r Hypothesis Testing About Two Population Variances (F Distribution)
		Summary
		Key Terms
		Notes
		Discussion Questions
		Numerical Problems
		Formulas
		Case Study
		Notes
	Chapter 12: Analysis of Variance and Experimental Designs
		12.1 Introduction
		12.2 Introduction to Experimental Designs
		12.3 Analysis of Variance
		12.4 Completely Randomized Design (One-Way ANOVA)
			12.4.1 Steps in Calculating SST (Total Sum of Squares) and Mean Squares in One-Way Analysis of Variance
			12.4.2 Applying the F-Test Statistic
			12.4.3 The ANOVA Summary Table
			12.4.4 Using MS Excel for Hypothesis Testing with the F Statistic for the Difference in Means of More Than Two Populations
			12.4.5 Using Minitab for Hypothesis Testing with the F Statistic for the Difference in the Means of More Than Two Populations
			12.4.6 Using SPSS for Hypothesis Testing with the F Statistic for the Difference in Means of More Than Two Populations
		12.5 Randomized Block Design
			12.5.1 Null and Alternative Hypotheses in a Randomized Block Design
			12.5.2 Applying the F-Test Statistic
			12.5.3 ANOVA Summary Table for Two-Way Classification
			12.5.4 Using MS Excel for Hypothesis Testing with the F Statistic in a Randomized Block Design
			12.5.5 Using Minitab for Hypothesis Testing with the F Statistic in a Randomized Block Design
		12.6 Factorial Design (Two-Way ANOVA)
			12.6.1 Null and Alternative Hypotheses in a Factorial Design
			12.6.2 Formulas for Calculating SST (Total Sum of Squares) and Mean Squares in a Factorial Design (Two-Way Analysis of Variance)
			12.6.3 Applying the F-Test Statistic
			12.6.4 ANOVA Summary Table for Two-Way ANOVA
			12.6.5 Using MS Excel for Hypothesis Testing with the F Statistic in a Factorial Design
			12.6.6 Using Minitab for Hypothesis Testing with the F Statistic in a Randomized Block Design
		Summary
		Key Terms
		Notes
		Discussion Questions
		Numerical Problems
		Formulas
		Case Study
		Notes
	Chapter 13: Hypothesis Testing for Categorical Data (Chi-Square Test)
		13.1 Introduction
		13.2 Defining x2-Test Statistic
			13.2.1 Conditions for Applying the x2 Test
		13.3 x2 Goodness-of-Fit Test
			13.3.1 Using MS Excel for Hypothesis Testing with x2 Statistic for Goodness-of-Fit Test
			13.3.2 Hypothesis Testing for a Population Proportion Using x2 Goodness-of-Fit Test as an Alternative Technique to the z-Test
		13.4 x2 Test of Independence: Two-Way Contingency Analysis
			13.4.1 Using Minitab for Hypothesis Testing with x2 Statistic for Test of Independence
		13.5 x2 Test for Population Variance
		13.6 x2 Test of Homogeneity
		Summary
		Key Terms
		Note
		Discussion Questions
		Numerical Problems
		Formulas
		Case Study
		Notes
	Chapter 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
		Notes
		Discussion Questions
		Formulas
		Numerical Problems
		Case Study
		Notes
	Chapter 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 (b1)
			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
		Notes
		Discussion Questions
		Numerical Problems
		Formulas
		Case Study
		Notes
	Chapter 16: Multivariate Analysis—I: Multiple Regression Analysis
		16.1 Introduction
		16.2 The Multiple Regression Model
		16.3 Multiple Regression Model with Two Independent Variables
		16.4 Determination of Coefficient of Multiple Determination (R2), Adjusted R2, and Standard Error of the Estimate
			16.4.1 Determination of Coefficient of Multiple Determination (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
		Notes
		Discussion Questions
		Numerical Problems
		Formulas
		Case Study
		Notes
	Chapter 17: Multivariate Analysis—II: Discriminant Analysis and Conjoint 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 Conjoint Analysis
			17.3.1 Introduction
			17.3.2 Concept of Performing Conjoint Analysis
			17.3.3 Steps in Conducting Conjoint Analysis
			17.3.4 Assumptions and Limitations of Conjoint Analysis
		References
		Summary
		Key Terms
		Notes
		Discussion Questions
		Case Study
		Notes
	Chapter 18: Multivariate Analysis—III: Factor Analysis, Cluster Analysis, Multidimensional Scaling, and Correspondence 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
		18.3 Multidimensional Scaling
			18.3.1 Introduction
			18.3.2 Some Basic Terms Used in Multidimensional Scaling
			18.3.3 The Process of Conducting Multidimensional Scaling
			18.3.4 Using SPSS for Multidimensional Scaling
		18.4 Correspondence Analysis
		References
		Summary
		Key Terms
		Notes
		Discussion Questions
		Case Study
		Notes
Part V: Result Presentation
	Chapter 19: Presentation of Result: Report Writing
		19.1 Introduction
		19.2 Organization of the Written Report
			19.2.1 Title Page
			19.2.2 Letter of Transmittal
			19.2.3 Letter of Authorization
			19.2.4 Table of Contents
			19.2.5 Executive Summary
			19.2.6 Body
			19.2.7 Appendix
		19.3 Tabular Presentation of Data
		19.4 Graphical Presentation of Data
			19.4.1 Bar Chart
			19.4.2 Pie Chart
			19.4.3 Histogram
			19.4.4 Frequency Polygon
			19.4.5 Ogive
			19.4.6 Scatter Plot
		19.5 Oral Presentation
		References
		Summary
		Key Terms
		Notes
		Discussion Questions
		Case Study
		Notes
Appendices
Glossary
Name Index
Subject Index