Data Management in R: A Guide for Social Scientists

This book is a valuable resource for social science researchers who need to manage data in R, providing step-by-step guidance and learning features to help them recode, combine, document, and import data from various sources.

\n Format: Hardback
\n Length: 272 pages
\n Publication date: 28 December 2020
\n Publisher: SAGE Publications Ltd
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This comprehensive guide is a must-have for social science researchers looking to effectively manage their data in R. It provides a step-by-step approach, covering essential topics such as recoding data, combining data from various sources, documenting data meticulously, and importing data from statistical packages beyond R. The book caters to both qualitative and quantitative data analysis, offering a wealth of supportive learning features to enhance understanding and proficiency.

Introduction:
Data management is a crucial aspect of social science research, as it ensures the accuracy, reliability, and integrity of the data collected. R, a powerful programming language for statistical analysis, offers a wide range of tools and techniques for data management. In this book, we will explore the essential steps and techniques for managing data in R, tailored specifically for social science researchers.

Recoding Data:
Recoding data is a common practice in social science research, where variables are transformed or coded to represent different concepts or categories. R provides various methods for recoding data, including factor analysis, logistic regression, and cluster analysis. We will discuss these methods in detail, providing examples and code snippets to illustrate their application.

Combining Data from Different Sources:
Social science research often involves collecting data from multiple sources, such as surveys, interviews, and administrative records. Combining data from different sources can be a complex task, but R provides several tools and techniques to facilitate this process. We will explore techniques such as merging data sets, combining data across time periods, and handling missing data.

Documenting Data:
Documenting data is essential for ensuring the transparency, reproducibility, and reuse of research findings. R provides various tools and techniques for documenting data, including data dictionaries, codebooks, and vignettes. We will discuss the importance of documenting data and provide guidance on how to create effective documentation.

Importing Data from Statistical Packages:
R is not limited to data management within R itself. It can also import data from a wide range of statistical packages, such as SPSS, Stata, and SAS. We will discuss the process of importing data from these packages and explore the advantages and disadvantages of each method.

Conclusion:
Data management in R is a complex but essential skill for social science researchers. This book provides a comprehensive and step-by-step guide to data management in R, covering recoding data, combining data from different sources, documenting data meticulously, and importing data from statistical packages beyond R. By following the guidelines and examples provided in this book, social science researchers can ensure the quality and integrity of their data, which is crucial for producing reliable and meaningful research findings.

\n Weight: 626g\n
Dimension: 177 x 251 x 21 (mm)\n
ISBN-13: 9781526459961\n \n