Michael N. Mitchell’s Data Management Using Stata 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.
Mitchell includes advice for those who would like to learn to write their own data-management Stata commands. 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.
Gostei bastante desse livro. Ele têm tudo que uma pessoa precisa para começar para trabalhar com stata: labelling, variable management, datasets operations, grouping operations e o básico de programação em stata, como loops e ado files.
O livro não é, porém, um guia compreensivo (no sentido anglicano do termo). Tudo é apresentado de forma bastante simples e sem aprofundamento. Mas é suficiente para iniciar o usuário no programa e o resto se resolve com statalist e stackoverflow. Para interessados em uma abordagem mais aprofundada e mais focada em econometria, outros manuais, como o do Cameron (Microeconometrics Using Stata), podem ser uma pedida mais interessante.
This book was absolutely wonderful for data management, especially dealing with missing values and observation, miss-classification tables and recorded variables. Furthermore, it has very clear language and well-organised. It is useful for those who are interesting to use STATA and statistical programming.