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.
Author(s): James E. Sallis, Geir Gripsrud, Ulf Henning Olsson, Ragnhild Silkoset
Series: Classroom Companion: Business
Edition: 1
Publisher: Springer
Year: 2021
Language: English
Commentary: TruePDF
Pages: 263
Tags: Statistics For Business, Management, Economics, Finance, Insurance, Management Education, Statistics, General Statistics For Social Sciences, Humanities, Law Statistics And Computing/Statistics Programs
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
