An introduction to data analysis : quantitative, qualitative and mixed methods

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

資料種別:: 図書
出版情報:: Los Angeles : Sage, 2018
形態:: xviii, 269 p. ; 25 cm
著者名:: Bergin, Tiffany <DA19234561>
ISBN:: 9781446295151 [144629515X] (: pbk)
9781446295144 [1446295141] (: hbk)
書誌ID:: BB27568891

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注記:

Includes bibliographical references (p. [243]-262) and index

主題:

Mathematical analysis--Data processing

言語:

英語

目次情報:

List of Tables

List of Figures

Preface

Acknowledgements

About the Author

Introducing Data / 1：

Chapter Overview / 1.1：

Data Surrounds Us / 1.2：

The Power of Data: Fog, Pollution, and Catastrophe in London / 1.3：

The Lingering Influence of Data: The Work of Alexis de Tocqueville / 1.4：

The Questions that Drive Data Analysis: The Work of Adolphe Quetelet / 1.5：

Defining 'Data' / 1.6：

From 'Data' to 'Big Data' / 1.7：

Concluding Thoughts / 1.8：

Summary / 1.9：

Further Reading / 1.10：

Discussion Questions / 1.11：

Thinking like a Data Analyst / 2：

Introduction: Developing a Rigorous, Reflective Attitude / 2.1：

Positivist and Interpretivist Frameworks / 2.3：

Contrasting Quantitative and Qualitative Approaches / 2.4：

Hypotheses and How to Test Them / 2.5：

Other Theoretical Approaches / 2.6：

Different Types of Validity / 2.7：

Triangulation / 2.8：

Recognizing Our Limitations / 2.9：

Finding, Collecting, and Organizing Data / 2.10：

Data Sources: An Introduction / 3.1：

Unexpected Data Sources: A Long-Term Perspective on Crime / 3.3：

Developing Research Questions and Hypotheses / 3.4：

Designing a Research Plan / 3.5：

Pilot Studies / 3.6：

Finding a Sample / 3.7：

Minimizing Sampling Error / 3.8：

Non-response / 3.9：

Missing Data / 3.10：

Thinking about Secondary Data / 3.11：

Ethics and Research Design / 3.12：

Introducing Quantitative Data Analysis / 3.13：

What is Quantitative Data Analysis? / 4.1：

Quantitative Data Analysis Approaches / 4.3：

Cross-Sectional and Longitudinal Data / 4.4：

Advantages and Disadvantages of Secondary Data / 4.5：

What Is a Variable? / 4.6：

Populations, Samples, and Statistical Inference / 4.7：

Descriptive Statistics / 4.8：

Measures of Central Tendency / 4.9：

Measures of Variability / 4.10：

The Sampling Distribution / 4.11：

The Normal Distribution / 4.12：

Introducing p-Values / 4.13：

Type I Error and Type II Error / 4.14：

One-Tailed Tests and Two-Tailed Tests / 4.15：

Beyond Significance Testing / 4.16：

Applying Quantitative Data Analysis: Correlations, t-Tests, and Chi-Square Tests / 4.17：

Introduction: Associations and Differences / 5.1：

Introducing Correlation / 5.3：

Understanding Covariance and Correlation / 5.4：

Conducting a Significance Test / 5.5：

Interpreting Pearson's Correlation Coefficient / 5.6：

Checking Assumptions / 5.7：

If Your Data Does Not Meet the Assumptions of Pearson's r / 5.8：

The t-Test: An Overview / 5.9：

Formulas for the t-Test / 5.10：

Interpreting t-Test Results: A Cautionary Note / 5.11：

If Your Data Does Not Meet the Assumptions of the t-Test / 5.12：

The Chi-Square Test of Association / 5.14：

Determining Significance / 5.15：

Testing Assumptions / 5.16：

If Your Data Does Not Meet the Assumptions of the Chi-Square Test of Association / 5.17：

Introducing Qualitative Data Analysis / 5.18：

What Makes Data 'Qualitative'? / 6.1：

Qualitative Data Analysis Approaches / 6.3：

Analytical Strategies / 6.4：

Coding: A Preview / 6.5：

Examples of Qualitative Studies / 6.6：

How to Select a Qualitative Approach / 6.7：

Applying Qualitative Data Analysis / 6.8：

Analysing Qualitative Data: An Overview / 7.1：

What Are You Going to Analyse? / 7.3：

Data Types / 7.4：

Non-Textual Analysis / 7.5：

Beginning Your Analysis / 7.6：

Launching the Coding Process / 7.7：

Predetermined and Spontaneous Codes / 7.8：

Complexities in the Coding Process / 7.9：

Theoretical Memos / 7.10：

Coding with CAQDAS / 7.11：

Presenting Findings / 7.12：

Introducing Mixed Methods: How to Synthesize Quantitative and Qualitative Data Analysis Techniques / 7.13：

What is Mixed-Methods Research? / 8.1：

Why Use Both Quantitative and Qualitative Methods? The Example of Excess Winter Mortality / 8.3：

Rationales for Using Mixed Methods / 8.4：

Types of Mixed-Methods Research / 8.5：

The Challenges of Crossing the Methodological Divide / 8.6：

Examples from Across the Disciplines / 8.7：

Communicating Findings and Visualizing Data / 8.8：

Wait… We're Not Done Yet? / 9.1：

Communicating Findings: General Principles / 9.3：

Think about Your Audience(s) / 9.4：

Different Venues for Communicating Findings / 9.5：

Data Visualization / 9.6：

Recommendations for Data Visualization / 9.7：

The Future of Data Visualization / 9.8：

Examples of innovative Data Visualizations / 9.9：

Conclusion: Becoming a Data Analyst / 9.10：

Becoming a Data Analyst: Key Principles / 10.1：

Concluding Thoughts: The Future of Data Analysis / 10.3：

Glossary / 10.4：

References

Index

List of Tables

List of Figures

Preface

Acknowledgements

About the Author

Introducing Data / 1：

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