Preparing Data for Analysis

Bianca Manago - Vanderbilt University, USA, Vanderbilt University

Volume: 199

Series:
Quantitative Applications in the Social Sciences

July 2025 | SAGE Publications, Inc

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. Author Bianca Manago discusses best practices for data preparation and analysis, emphasizing the importance of prioritizing transparency, best practices for writing script files, and file naming and organization. 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.

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ISBN: 9781071919569 Paperback Suggested Retail Price: $51.00 Bookstore Price: $40.80

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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

Supplements

Student Study Site
Datasets, script files, and output files in both R and Stata are available to download from the accompanying website.

Key features

Covers a range of topics including data compilation, variable naming and labeling, data examination, and variable re-coding and transformations, among others.
Discusses best practices for data preparation and analysis, emphasizing the importance of prioritizing transparency, best practices for writing script files, and file naming and organization.
Provides two example projects and datasets are used to illustrate the methods in the book.
Datasets, script files, and output files in both R and Stata are available to download from the accompanying website.

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Preparing Data for Analysis
From Raw to Ready

Supplements

Sage College Publishing

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