Data Management Using Stata - Mitchell, Michael N

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Résumé :
Data Management Using Stata : A Practical Handbook focuses on tasks that bridge the gap between raw data and statistical analysis. The chapters are organized by data-management areas : reading and writing datasets, cleaning data, labeling datasets, creating variables, combining dataseis, processing observations across subgroups, changing the shape of dataseis, and programming for data management. Within each chapter, each section is a self-contained lesson illustrating a particular data-management task (e.g., 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 fast. In addition to the "nuts and bolts" 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-saving?there is a good chance that even the experienced user will learn some new tricks. Michael Mitchell is a senior statistician in health services research. He is the author of A Visual Guide to Stata Graphics, 2nd Edition, as well as the Stata Tidbit of the Week. Previously, he worked for 12 years as a statistical consultant and manager of the UCLA ATS Statistical Consulting Group. There, he envisioned the UCLA Statistical Consulting Resources web site and wrote hundreds of web pages about Stata.

Biographie:
Michael N. Mitchell is a senior statistician in health services research. For 12 years, he worked in the Statistical Consulting Group of the UCLA Academic Technology Services.

Sommaire:
Introduction Using this book Overview of this book Listing observations in this book Reading and Writing Datasets Introduction Reading Stata datasets Saving Stata datasets Reading comma-separated and tab-separated files Reading space-separated files Reading fixed-column files Reading fixed-column files with multiple lines of raw data per observation Reading SAS XPORT files Common errors reading files Entering data directly into the Stata Data Editor Saving comma-separated and tab-separated files Saving space-separated files Saving SAS XPORT files Data Cleaning Introduction Double data entry Checking individual variables Checking categorical by categorical variables Checking categorical by continuous variables Checking continuous by continuous variables Correcting errors in data Identifying duplicates Final thoughts on data cleaning Labeling Datasets Introduction Describing datasets Labeling variables Labeling values Labeling utilities Labeling variables and values in different languages Adding comments to your dataset using notes Formatting the display of variables Changing the order of variables in a dataset Creating Variables Introduction Creating and changing variables Numeric expressions and functions String expressions and functions Recoding Coding missing values Dummy variables Date variables Date-and-time variables Computations across variables Computations across observations More examples using the egen command Converting string variables to numeric variables Converting numeric variables to string variables Renaming and ordering variables Combining Datasets Introduction Appending: Appending datasets Appending: Problems Merging: One-to-one match-merging Merging: One-to-many match-merging Merging: Merging multiple datasets Merging: Update merges Merging: Additional options when merging datasets Merging: Problems merging datasets Joining datasets Crossing datasets Processing Observations across Subgroups Introduction Obtaining separate results for subgroups Computing values separately by subgroups Computing values within subgroups: Subscripting observations Computing values within subgroups: Computations across observations Computing values within subgroups: Running sums Computing values within subgroups: More examples Comparing the by and tsset commands Changing the Shape of Your Data Introduction Wide and long datasets Introduction to reshaping long to wide Reshaping long to wide: Problems Introduction to reshaping wide to long Reshaping wide to long: Problems Multilevel datasets Collapsing datasets Programming for Data Management Introduction Tips on long-term goals in data management Executing do-files and making log files Automating data checking Combining do-files Introducing Stata macros Manipulating Stata macros Repeating commands by looping over variables Repeating commands by looping over numbers Repeating commands by looping over anything Accessing results saved from Stata commands Saving results of estimation commands as data Writing Stata programs Additional Resources Online resources for this book Finding and installing additional programs More online resources Appendix: Common elements Index

The author uses a learning by example approach in the book. Overall this works well ... -Morteza Marzjarani, The American Statistician, November 2011