This book focuses on the process of preparing raw data for analysis-commonly known as data cleaning. It covers a range of topics including data compilation, variable naming and labeling, data examination, and variable re-coding and transformations, among others. Two example projects and datasets are used to illustrate the methods in the book, and the datasets, script files, and output files in both R and Stata are available to download from the accompanying website.
This book focuses on the process of preparing raw data for analysis-commonly known as data cleaning. It covers a range of topics including data compilation, variable naming and labeling, data examination, and variable re-coding and transformations, among others. Two example projects and datasets are used to illustrate the methods in the book, and the datasets, script files, and output files in both R and Stata are available to download from the accompanying website.
Bianca Manago is an Assistant Professor of Sociology at Vanderbilt University. She has taught both graduate and undergraduate statistics courses and has also taught workshops on data cleaning and workflow for Statistical Horizons. She received her doctorate in sociology and master's in applied statistics from Indiana University. She also has a master's in sociology and undergraduate degrees in sociology and philosophy from Texas A&M University.
Inhaltsangabe
Preface Acknowledgments About the Author Part: 1 Introduction Chapter 1: Data Preparation: The Need for Strategy and Transparency Importance of data preparation Transparency Tools for transparency Summary Chapter 2: Software and Script File Considerations Software considerations Script file robustness and legibility Summary Chapter 3: File Organization and Naming Dual workflow and primary script files File structure and document organization Naming: files, folders, and more Summary Part 2: CLEANR Method Chapter 4: Introduction Data preparation steps Rationale behind the order Reconsider the rules of ordering Summary Chapter 5: Compiling Data Preparing for data compilation Collecting data Downloading data Steps between downloading data and uploading data Uploading and importing data Dropping and keeping variables Appending and merging data frames Re-shaping data frames Summary Chapter 6: Labeling and Naming Variables and Values Variable naming Variable and value labels Summary Chapter 7: Examining Data Data quality indicators Respondent quality Characteristics of data sample Summary Chapter 8: Addressing Data Problems Low quality data Anomalous data Missing data strategies Summary Chapter 9: New Variable Creation Composite (scale) variables Standardizing through use of proportions, percents, and rates Integer/label encoding Re-coding/discretization Summary Chapter 10: Re-configure, Re-examine, and Review Re-configuring data Re-examining data Code review Summary Part 3: Review and Conclusion Chapter 11: CLEANR in practice General Social Survey Systematic Review Summary Chapter 12: Conclusion Section I: Best Practices Section II: CLEANR Method Section III: Conclusion Conclusion References
Preface Acknowledgments About the Author Part: 1 Introduction Chapter 1: Data Preparation: The Need for Strategy and Transparency Importance of data preparation Transparency Tools for transparency Summary Chapter 2: Software and Script File Considerations Software considerations Script file robustness and legibility Summary Chapter 3: File Organization and Naming Dual workflow and primary script files File structure and document organization Naming: files, folders, and more Summary Part 2: CLEANR Method Chapter 4: Introduction Data preparation steps Rationale behind the order Reconsider the rules of ordering Summary Chapter 5: Compiling Data Preparing for data compilation Collecting data Downloading data Steps between downloading data and uploading data Uploading and importing data Dropping and keeping variables Appending and merging data frames Re-shaping data frames Summary Chapter 6: Labeling and Naming Variables and Values Variable naming Variable and value labels Summary Chapter 7: Examining Data Data quality indicators Respondent quality Characteristics of data sample Summary Chapter 8: Addressing Data Problems Low quality data Anomalous data Missing data strategies Summary Chapter 9: New Variable Creation Composite (scale) variables Standardizing through use of proportions, percents, and rates Integer/label encoding Re-coding/discretization Summary Chapter 10: Re-configure, Re-examine, and Review Re-configuring data Re-examining data Code review Summary Part 3: Review and Conclusion Chapter 11: CLEANR in practice General Social Survey Systematic Review Summary Chapter 12: Conclusion Section I: Best Practices Section II: CLEANR Method Section III: Conclusion Conclusion References
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