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Comment from the Stata technical group

Michael N. Mitchell’s Data Management Using Stata: A Practical Handbook, Second Edition comprehensively covers data management tasks, from those a beginning statistician would need to those hard-to-verbalize tasks that can confound an experienced user. Mitchell does this all in simple language with illustrative examples.

The book is modular in structure, with modules based on data management tasks rather than on clusters of commands. This format is helpful because it allows readers to find just what they need to solve a problem at hand. To complement this format, the book is in a style that will teach even sporadic readers good habits in data management, even if the reader chooses to read chapters out of order.

Throughout the book, Mitchell subtly emphasizes the absolute necessity of reproducibility and an audit trail. Instead of stressing programming esoterica, Mitchell reinforces simple habits and points out the time savings gained by being careful. Mitchell’s experience in UCLA’s Academic Technology Services clearly drives much of his advice.

The second edition brings updates needed for features added to Stata versions since Stata 10: reading and writing Microsoft Excel files, working with Unicode properly, and using frames. Mitchell also added a chapter showing how to build your own utility programs to simplify and automate routine tasks, easing code maintenance and aiding uniformity across projects.

New users will learn everything they need to import, clean, and prepare data for first analyses in Stata. Even experienced users will learn new tricks and new ways to approach data management problems.

This is a great book–thoroughly recommended for anyone interested in data management using Stata.

About the author

Michael Mitchell is a senior statistician working in the area of sleep research as well as working on prevention of child maltreatment with the Children’s Data Network. He is the author of three other Stata Press books—A Visual Guide to Stata GraphicsInterpreting and Visualizing Regression Models Using Stata, and Stata for the Behavioral Sciences.