Data Management Using Stata
This second edition of Data Management Using Stata focuses on tasks that bridge the gap between raw data and statistical analysis. It has been updated throughout to reflect new data management features that have been added over the last 10 years. Such features include the ability to read and write a wide variety of file formats, the ability to write highly customised Excel files, the ability to have multiple Stata datasets open at once, and the ability to store and manipulate string variables stored as Unicode.
Further, this new edition includes a new chapter illustrating how to write Stata programs for solving data management tasks. As in the original edition, the chapters are organised by data management areas: reading and writing datasets, cleaning data, labelling datasets, creating variables, combining datasets, processing observations across subgroups, changing the shape of datasets, and programming for data management. Within each chapter, each section is a self-contained lesson illustrating a particular data management task (for instance, creating date variables or automating error checking) via examples. This modular design allows you to quickly identify and implement the most common data management tasks without having to read background information first. In addition to the ānuts and boltsā examples, author Michael Mitchell alerts users to common pitfalls (and how to avoid them) and provides strategic data management advice.
This book can be used as a quick reference for solving problems as they arise or can be read as a means for learning comprehensive data management skills. New users will appreciate this book as a valuable way to learn data management, while experienced users will find this information to be handy and time savingāthere is a good chance that even the experienced user will learn some new tricks.
This second edition of Data Management Using Stata focuses on tasks that bridge the gap between raw data and statistical analysis. It has been updated throughout to reflect new data management features that have been added over the last 10 years. Such features include the ability to read and write a wide variety of file formats, the ability to write highly customised Excel files, the ability to have multiple Stata datasets open at once, and the ability to store and manipulate string variables stored as Unicode.
Further, this new edition includes a new chapter illustrating how to write Stata programs for solving data management tasks. As in the original edition, the chapters are organised by data management areas: reading and writing datasets, cleaning data, labelling datasets, creating variables, combining datasets, processing observations across subgroups, changing the shape of datasets, and programming for data management. Within each chapter, each section is a self-contained lesson illustrating a particular data management task (for instance, creating date variables or automating error checking) via examples. This modular design allows you to quickly identify and implement the most common data management tasks without having to read background information first. In addition to the ānuts and boltsā examples, author Michael Mitchell alerts users to common pitfalls (and how to avoid them) and provides strategic data management advice.
This book can be used as a quick reference for solving problems as they arise or can be read as a means for learning comprehensive data management skills. New users will appreciate this book as a valuable way to learn data management, while experienced users will find this information to be handy and time savingāthere is a good chance that even the experienced user will learn some new tricks.
Original: $84.16
-65%$84.16
$29.46Description
This second edition of Data Management Using Stata focuses on tasks that bridge the gap between raw data and statistical analysis. It has been updated throughout to reflect new data management features that have been added over the last 10 years. Such features include the ability to read and write a wide variety of file formats, the ability to write highly customised Excel files, the ability to have multiple Stata datasets open at once, and the ability to store and manipulate string variables stored as Unicode.
Further, this new edition includes a new chapter illustrating how to write Stata programs for solving data management tasks. As in the original edition, the chapters are organised by data management areas: reading and writing datasets, cleaning data, labelling datasets, creating variables, combining datasets, processing observations across subgroups, changing the shape of datasets, and programming for data management. Within each chapter, each section is a self-contained lesson illustrating a particular data management task (for instance, creating date variables or automating error checking) via examples. This modular design allows you to quickly identify and implement the most common data management tasks without having to read background information first. In addition to the ānuts and boltsā examples, author Michael Mitchell alerts users to common pitfalls (and how to avoid them) and provides strategic data management advice.
This book can be used as a quick reference for solving problems as they arise or can be read as a means for learning comprehensive data management skills. New users will appreciate this book as a valuable way to learn data management, while experienced users will find this information to be handy and time savingāthere is a good chance that even the experienced user will learn some new tricks.
