The Data Detective's Toolkit - Chantala, Kim

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Résumé :
Reduce the cost and time of cleaning, managing, and preparing research data while also improving data quality! Have you ever wished there was an easy way to reduce your workload and improve the quality of your data? The Data Detective's Toolkit: Cutting-Edge Techniques and SAS Macros to Clean, Prepare, and Manage Data will help you automate many of the labor-intensive tasks needed to turn raw data into high-quality, analysis-ready data. You will find the right tools and techniques in this book to reduce the amount of time needed to clean, edit, validate, and document your data. These tools include SAS macros as well as ingenious ways of using SAS procedures and functions. The innovative logic built into the book's macro programs enables you to monitor the quality of your data using information from the formats and labels created for the variables in your data set. The book explains how to harmonize data sets that need to be combined and automate data cleaning tasks to detect errors in data including out-of-range values, inconsistent flow through skip paths, missing data, no variation in values for a variable, and duplicates. By the end of this book, you will be able to automatically produce codebooks, crosswalks, and data catalogs.

Biographie:
Kim Chantala is a Programmer Analyst in the Research Computing Division at RTI International with over 25 years of experience in managing and analyzing research data. Before joining RTI International, she was a data analyst at the University of North Carolina at Chapel Hill. In addition to providing data management and analytical services at the University, she taught workshops on analyzing survey data, focusing on the problems of sample weights and design effects. Kim believes that the real challenge in data analysis is bridging the gap between raw or acquired data and data that is ready to analyze. This inspired her to develop computerized data management tools revolutionizing the way data is prepared, allowing users to improve the quality of their data while lowering the cost of data preparation.
Kim earned a BS in Engineering Physics from the Colorado School of Mines and an MS in Biometrics from the University of Colorado.