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Webinar Series: An Introduction to Data Management and Cleaning for Analysis
April 4, 2017 - April 20, 2017
DATES AND TIMES
This webinar series contains six, two-hour sessions delivered from 10:00am – 12:00pm PST each session.
Session 1: Tue April 4 | Session 2: Thurs April 6 | Session 3: Tues April 11 | Session 4: Thurs April 13 | Session 5: Tues April 18 | Session 6: Thurs April 20
This webinar series provides an overview of basic data management and data cleaning techniques using SAS software.
In taking this course, you will learn how to develop a systematic approach to managing and cleaning your data for statistical analyses. This approach involves understanding the big picture first, and then developing a strategy for translating the big picture into concrete problem-solving steps. The workflow involved in these steps will be illustrated using a synthesized administrative data set and honed through a variety of applied exercises. During the course, you will be provided with access to a variety of practical tools that will ensure you will develop a sustainable and effective workflow for all of your future data analysis projects: SAS code, case studies, web resources and more. The overall goal of the course is to give you the conceptual and practical tools you need to handle your data preparation needs with confidence.
Homework activities will be provided for practice between sessions.
Prior required knowledge
Participants will be expected to have familiarity with the use of Administrative Data, basic knowledge of SAS functions (i.e.: descriptive statistics, merging and sorting) and an understanding of logistic regression.
By the end of this webinar series, participants will be able to:
- Identify key types of data errors commonly found in the use of administrative data
- Address and correct data errors using a systematic process
- Subset, filter and aggregate data in preparation for statistical analyses;
- Define the role of key variables for statistical analyses;
- Recode qualitative variables as required
- Transform quantitative variables as required