An introduction to data analysis : quantitative, qualitative and mixed methods / Tiffany Bergin.

Enhanced descriptions from Syndetics:

Covering the general process of data analysis to finding, collecting, organizing, and presenting data, this book offers a complete introduction to the fundamentals of data analysis.

Using real-world case studies as illustrations, it helps readers understand theories behind and develop techniques for conducting quantitative, qualitative, and mixed methods data analysis. With an easy-to-follow organization and clear, jargon-free language, it helps readers not only become proficient data analysts, but also develop the critical thinking skills necessary to assess analyses presented by others in both academic research and the popular media.

It includes advice on:

- Data analysis frameworks

- Validity and credibility of data

- Sampling techniques

- Data management

- The big data phenomenon

- Data visualisation

- Effective data communication

Whether you are new to data analysis or looking for a quick-reference guide to key principles of the process, this book will help you uncover nuances, complexities, patterns, and relationships among all types of data.

Table of contents provided by Syndetics

• List of Tables (p. xi)
• List of Figures (p. xiii)
• Preface (p. xv)
• Acknowledgements (p. xix)
• About the Author (p. xxi)
• 1 Introducing Data (p. 1)
• 1.1 Chapter Overview (p. 2)
• 1.2 Data Surrounds Us (p. 2)
• 1.3 The Power of Data: Fog, Pollution, and Catastrophe in London (p. 2)
• 1.4 The Lingering Influence of Data: The Work of Alexis de Tocqueville (p. 3)
• 1.5 The Questions that Drive Data Analysis: The Work of Adolphe Quetelet (p. 4)
• 1.6 Defining 'Data' (p. 8)
• 1.7 From 'Data' to 'Big Data' (p. 10)
• 1.8 Concluding Thoughts (p. 11)
• 1.9 Summary (p. 12)
• 1.10 Further Reading (p. 12)
• 1.11 Discussion Questions (p. 13)
• 2 Thinking like a Data Analyst (p. 15)
• 2.1 Chapter Overview (p. 16)
• 2.2 Introduction: Developing a Rigorous, Reflective Attitude (p. 16)
• 2.3 Positivist and Interpretivist Frameworks (p. 17)
• 2.4 Contrasting Quantitative and Qualitative Approaches (p. 19)
• 2.5 Hypotheses and How to Test Them (p. 21)
• 2.6 Other Theoretical Approaches (p. 24)
• 2.7 Different Types of Validity (p. 26)
• 2.8 Triangulation (p. 29)
• 2.9 Recognizing Our Limitations (p. 29)
• 2.10 Concluding Thoughts (p. 30)
• 2.11 Summary (p. 31)
• 2.12 Further Reading (p. 31)
• 2.13 Discussion Questions (p. 32)
• 3 Finding, Collecting, and Organizing Data (p. 33)
• 3.1 Chapter Overview (p. 34)
• 3.2 Data Sources: An Introduction (p. 34)
• 3.3 Unexpected Data Sources: A Long-Term Perspective on Crime (p. 35)
• 3.4 Developing Research Questions and Hypotheses (p. 36)
• 3.5 Designing a Research Plan (p. 38)
• 3.6 Pilot Studies (p. 38)
• 3.7 Finding a Sample (p. 39)
• 3.8 Minimizing Sampling Error (p. 46)
• 3.9 Non-response (p. 47)
• 3.10 Missing Data (p. 48)
• 3.11 Thinking about Secondary Data (p. 51)
• 3.12 Ethics and Research Design (p. 53)
• 3.13 Concluding Thoughts (p. 60)
• 3.14 Summary (p. 60)
• 3.15 Further Reading (p. 61)
• 3.16 Discussion Questions (p. 62)
• 4 Introducing Quantitative Data Analysis (p. 65)
• 4.1 Chapter Overview (p. 66)
• 4.2 What is Quantitative Data Analysis? (p. 66)
• 4.3 Quantitative Data Analysis Approaches (p. 67)
• 4.4 Cross-Sectional and Longitudinal Data (p. 70)
• 4.5 Advantages and Disadvantages of Secondary Data (p. 71)
• 4.6 What Is a Variable? (p. 72)
• 4.7 Populations, Samples, and Statistical Inference (p. 74)
• 4.8 Descriptive Statistics (p. 77)
• 4.9 Measures of Central Tendency (p. 77)
• 4.10 Measures of Variability (p. 79)
• 4.11 The Sampling Distribution (p. 80)
• 4.12 The Normal Distribution (p. 81)
• 4.13 Introducing p-Values (p. 82)
• 4.14 Type I Error and Type II Error (p. 83)
• 4.15 One-Tailed Tests and Two-Tailed Tests (p. 84)
• 4.16 Beyond Significance Testing (p. 85)
• 4.17 Concluding Thoughts (p. 86)
• 4.18 Summary (p. 86)
• 4.19 Further Reading (p. 87)
• 4.20 Discussion Questions (p. 87)
• 5 Applying Quantitative Data Analysis: Correlations, t-Tests, and Chi-Square Tests (p. 89)
• 5.1 Chapter Overview (p. 90)
• 5.2 Introduction: Associations and Differences (p. 90)
• 5.3 Introducing Correlation (p. 91)
• 5.4 Understanding Covariance and Correlation (p. 92)
• 5.5 Conducting a Significance Test (p. 98)
• 5.6 Interpreting Pearson's Correlation Coefficient (p. 100)
• 5.7 Checking Assumptions (p. 101)
• 5.8 If Your Data Does Not Meet the Assumptions of Pearson's r (p. 102)
• 5.9 The t-Test: An Overview (p. 103)
• 5.10 Formulas for the t-Test (p. 106)
• 5.11 Interpreting t-Test Results: A Cautionary Note (p. 116)
• 5.12 Checking Assumptions (p. 116)
• 5.13 If Your Data Does Not Meet the Assumptions of the t-Test (p. 117)
• 5.14 The Chi-Square Test of Association (p. 118)
• 5.15 Determining Significance (p. 123)
• 5.16 Testing Assumptions (p. 124)
• 5.17 If Your Data Does Not Meet the Assumptions of the Chi-Square Test of Association (p. 125)
• 5.18 Concluding Thoughts (p. 126)
• 5.19 Summary (p. 126)
• 5.20 Further Reading (p. 127)
• 5.21 Discussion Questions (p. 128)
• 6 Introducing Qualitative Data Analysis (p. 129)
• 6.1 Chapter Overview (p. 130)
• 6.2 What Makes Data 'Qualitative'? (p. 130)
• 6.3 Qualitative Data Analysis Approaches (p. 131)
• 6.4 Analytical Strategies (p. 138)
• 6.5 Coding: A Preview (p. 140)
• 6.6 Examples of Qualitative Studies (p. 142)
• 6.7 How to Select a Qualitative Approach (p. 143)
• 6.8 Concluding Thoughts (p. 145)
• 6.9 Summary (p. 145)
• 6.10 Further Reading (p. 145)
• 6.11 Discussion Questions (p. 147)
• 7 Applying Qualitative Data Analysis (p. 149)
• 7.1 Chapter Overview (p. 150)
• 7.2 Analysing Qualitative Data: An Overview (p. 150)
• 7.3 What Are You Going to Analyse? (p. 151)
• 7.4 Data Types (p. 151)
• 7.5 Non-Textual Analysis (p. 153)
• 7.6 Beginning Your Analysis (p. 154)
• 7.7 Launching the Coding Process (p. 155)
• 7.8 Predetermined and Spontaneous Codes (p. 155)
• 7.9 Complexities in the Coding Process (p. 161)
• 7.10 Theoretical Memos (p. 162)
• 7.11 Coding with CAQDAS (p. 163)
• 7.12 Presenting Findings (p. 167)
• 7.13 Concluding Thoughts (p. 169)
• 7.14 Summary (p. 170)
• 7.15 Further Reading (p. 171)
• 7.16 Discussion Questions (p. 171)
• 8 Introducing Mixed Methods: How to Synthesize Quantitative and Qualitative Data Analysis Techniques (p. 175)
• 8.1 Chapter Overview (p. 176)
• 8.2 What is Mixed-Methods Research? (p. 176)
• 8.3 Why Use Both Quantitative and Qualitative Methods? The Example of Excess Winter Mortality (p. 177)
• 8.4 Rationales for Using Mixed Methods (p. 180)
• 8.5 Types of Mixed-Methods Research (p. 183)
• 8.6 The Challenges of Crossing the Methodological Divide (p. 184)
• 8.7 Examples from Across the Disciplines (p. 187)
• 8.8 Concluding Thoughts (p. 190)
• 8.9 Summary (p. 191)
• 8.10 Further Reading (p. 191)
• 8.11 Discussion Questions (p. 192)
• 9 Communicating Findings and Visualizing Data (p. 193)
• 9.1 Chapter Overview (p. 194)
• 9.2 Wait... We're Not Done Yet? (p. 194)
• 9.3 Communicating Findings: General Principles (p. 194)
• 9.4 Think about Your Audience(s) (p. 196)
• 9.5 Different Venues for Communicating Findings (p. 197)
• 9.6 Data Visualization (p. 199)
• 9.7 Recommendations for Data Visualization (p. 218)
• 9.8 The Future of Data Visualization (p. 219)
• 9.9 Examples of innovative Data Visualizations (p. 220)
• 9.10 Concluding Thoughts (p. 221)
• 9.11 Summary (p. 222)
• 9.12 Further Reading (p. 222)
• 9.13 Discussion Questions (p. 223)
• 10 Conclusion: Becoming a Data Analyst (p. 225)
• 10.1 Chapter Overview (p. 226)
• 10.2 Becoming a Data Analyst: Key Principles (p. 226)
• 10.3 Concluding Thoughts: The Future of Data Analysis (p. 232)
• 10.4 Summary (p. 232)
• 10.5 Further Reading (p. 233)
• 10.6 Discussion Questions (p. 234)
• Glossary (p. 235)
• References (p. 243)
• Index (p. 263)

Author notes provided by Syndetics