18 August 2017
Introduction to Data Analysis Using Stata
This course is tailored to academics and research professionals that are new to statistical computing and/or Stata. Users that are familiar with earlier versions of Stata and wish to update their skills will also profit from this course.
In the first part of the course we will cover the facilities and basic mechanics of Stata including its syntax. In addition, we will review available help and support features (online and offline). Subsequently, we will immerge into hands-on data management, data analysis, and visualizing of results. Special attention will be devoted on how to use Stata within an integrated workflow and how to obtain publication ready results (tables and graphs) from Stata. If time permits and if participants are familiar with basic procedures, we will advance to special
topics such as understanding and interpreting interactions in linear and non-linear models (e.g., logit models), or managing of longitudinal or hierarchical data. Topics will be selected taking into account participants' main interests.
This course will be interactive and case-based in nature. All necessary materials will be provided. By the end of this course, participants will be able to effectively use Stata for common statistical procedures and know how to go about more advanced applications.
Dr. Reinhard Schunck is head of GESIS Training and the scientific coordinator of the GESIS Spring Seminar. He has extensive experience using Stata in research and teaching and has published in The Stata Journal.
Dr. Klaus Pforr is postdoctoral researcher
- Users new to statistical computing
- Advanced academics and research professionals familiar with other software
- Former Stata users who want to refresh their knowledge
By the end of the course participants will:
- be familiar with Stata's interface and facilities;
- understand how to integrate Stata into their research process to efficiently create reproducible and publication ready results;
- know how to solve common data management problems while seamlessly documenting all modifications of the data;
- be able to perform typical descriptive and inferential statistical procedures and use graphs to effectively communicate their results;
- have transferred the covered topics to their own research problems;
- know how to proceed from here and how to get additional support if needed.
EUR 100: Student/PhD student rate.
EUR 140: Academic/non-profit rate.
The rates include the tuition fee, course materials, access to library and IT facilities and coffee/tea.