Research Methods and Data Analysis for Business Decisions: A Primer Using SPSS (Classroom Companion: Business)

Preface
Contents
Part I: Designing the Study
	1: Research Methods and Philosophy of Science
		1.1 Introduction
		1.2 Two Alternatives: Constructivism Vs. Positivism
		1.3 Falsificationism, Theories, and Hypotheses
		1.4 Decision Processes
			1.4.1 Models of Decision-Making
			1.4.2 Models of Politics and Power
			1.4.3 Models of Ill-Defined Preferences and Fluid Participation
		1.5 Summary
		References
	2: The Research Process and Problem Formulation
		2.1 Introduction
		2.2 Problems, Opportunities, and Symptoms
		2.3 The Research Purpose and Research Question(s)
			Example 2.1
			Example 2.2
		2.4 The Research Process
		2.5 Summary
		Reference
	3: Research Design
		3.1 Introduction
		3.2 Exploratory Design
			3.2.1 Focus Groups
			3.2.2 Individual In-Depth Interviews
			3.2.3 Other Techniques
		3.3 Descriptive Design
			3.3.1 Survey Research with Questionnaires
			3.3.2 Observation and Diaries
		3.4 Causal Design
			3.4.1 True Experiments (Lab or Field)
			3.4.2 Quasi-Experiments
			3.4.3 Lab Experiments
			3.4.4 Field Experiments
			3.4.5 Internal and External Validity in Experiments
		3.5 Choice of Research Design
			3.5.1 Using Theory
		3.6 Validity and Reliability
		3.7 Summary
		Reference
Part II: Data Collection
	4: Secondary Data and Observation
		4.1 Introduction
		4.2 Main Types of Secondary Data
		4.3 Internal and External Sources
			4.3.1 Big Data
			4.3.2 Public Sources
			4.3.3 Scholarly Literature
			4.3.4 Standardized Research Services
				What Do We Mean by Standardization?
				Standardized Panel Data
		4.4 Sources of Error in Secondary Data
		4.5 Collecting Data by Observation
			4.5.1 Types of Observation
			4.5.2 Measuring Emotions by Observation
			4.5.3 Using the Observation Method
		4.6 Summary
		Reference
	5: Qualitative Methods
		5.1 Introduction
		5.2 Focus Groups
		5.3 Individual In-Depth Interviews
		5.4 Projective Techniques
		5.5 Content Analysis of Social Media
		5.6 Problem Formulation and Qualitative Data Analysis
			Example 5.1
		5.7 Summary
		References
	6: Questionnaire Surveys
		6.1 Introduction
		6.2 Constructs and Operationalization
		6.3 Validity
			6.3.1 Content Validity
			6.3.2 Construct Validity
			6.3.3 Face Validity
			6.3.4 Statistical Conclusion Validity
		6.4 Reliability
		6.5 Measurement Scales
			6.5.1 Parametric Versus Nonparametric Methods
		6.6 Attitude and Perception Measurement
		6.7 Scale Values
		6.8 Question Formulation and Order
			6.8.1 Question Design
			6.8.2 Pre-test
		6.9 Collecting the Data
			6.9.1 Personal Interviews
			6.9.2 Online Solutions
			6.9.3 Telephone Interviews
			6.9.4 Postal Surveys
		6.10 Summary
		References
	7: Sampling
		7.1 Introduction
		7.2 Define the Population and Sampling Frame
			7.2.1 Sampling Frame
		7.3 Sampling Method
			7.3.1 Probability Samples
			7.3.2 Non-probability Samples
		7.4 Sample Size
		7.5 Error Sources
			7.5.1 Missing Observations
		7.6 Summary
		References
Part III: Quantitative Data Analysis
	8: Simple Analysis Techniques
		8.1 Introduction
		8.2 Using Software
		8.3 Simple Analysis Techniques
		8.4 Cleaning the Data
		8.5 Analytical Techniques for One Variable
			Example 8.1 Descriptive Statistics
		8.6 Analytical Techniques for Relationships between Variables
			Example 8.2
		8.7 Summary
		References
	9: Hypothesis Testing
		9.1 Introduction
		9.2 Hypothesis Tests and Error
		9.3 The T-test
			Example 9.1
			Example 9.2
			Example 9.3
		9.4 Analysis of Variance: One-way ANOVA
		9.5 Chi-square Test (Chi2)
		9.6 Testing Correlation Coefficients
		9.7 Summary
	10: Regression Analysis
		10.1 Introduction
		10.2 Simple Regression Analysis
		10.3 Estimating Regression Parameters
			Example 10.1
			Example 10.2
		10.4 The T-test
			Example 10.3
		10.5 Multiple Regression Analysis
		10.6 Explained Variance
		10.7 The ANOVA Table and F-test
		10.8 Too Many or Too Few Independent Variables
		10.9 Regression with Dummy Variables
		10.10 Dummy Regression: An Alternative Analysis of Covariance ANCOVA
		10.11 The Classic Assumptions of Multiple Regression
		10.12 Summary
		References
	11: Cluster Analysis and Segmentation
		11.1 Introduction
		11.2 Similarities Between Groups in the Data
		11.3 Two Branches of Cluster Methods
		11.4 Hierarchical Clustering
		11.5 Non-hierarchical Clustering (K-Means)
		11.6 Interpretation and Further Use of the Clusters
		11.7 Summary
	12: Factor Analysis
		12.1 Introduction
		12.2 Exploratory Factor Analysis
		12.3 Principal Component Analysis
		12.4 Running Exploratory Factor Analysis
		12.5 Unidimensionality
		12.6 Confirmatory Factor Analysis
		12.7 Summary
		References
Part IV: Reporting
	13: Reporting Findings
		13.1 Introduction
		13.2 The Report
			13.2.1 Writing Style
		13.3 The Structure of a Research Report
			13.3.1 Referencing
		13.4 Implementation
		13.5 Advice for Students
		13.6 Summary
		Reference
Index