Research Methods And Data Analysis For Business Decisions: A Primer Using SPSS

Description

This introductory textbook presents research methods and data analysis tools in non-technical language. It explains the research process and the basics of qualitative and quantitative data analysis, including procedures and methods, analysis, interpretation, and applications using hands-on data examples in QDA Miner Lite and IBM SPSS Statistics software. The book is divided into four parts that address study and research design; data collection, qualitative methods and surveys; statistical methods, including hypothesis testing, regression, cluster and factor analysis; and reporting. The intended audience is business and social science students learning scientific research methods, however, given its business context, the book will be equally useful for decision-makers in businesses and organizations. 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