Summer training in statistical sampling at University of Michigan

https://statmodeling.stat.columbia.edu/2020/02/12/summer-training-in-statistical-sampling-at-university-of-michigan/

Yajuan points us to this summer program:

The 53rd Sampling Program for Survey Statisticians will be offered by the Survey Research Center at the University of Michigan’s Institute for Social Research from June 3 to July 31, 2020.

Founded by Professor Leslie Kish in 1961, the Sampling Program is devoted to training statisticians in sound probability sampling methods for diverse complex survey research problems. Since its inception, the Sampling Program has trained hundreds of participants from over 100 countries.

This intensive eight-week training program covers the principles and practice of survey sampling, the analysis of complex sample survey data, and application of sampling methods in many settings in three courses:

• Methods of Survey Sampling
• Analysis of Complex Sample Survey Data
• Workshop in Survey Sampling Techniques

Courses are conducted by Survey Research Center faculty and staff, and expert guest instructors, and cover topics ranging from traditional survey sampling and analysis methods to cutting-edge methodology, including utilizing machine learning and big data in survey settings and non-probability sampling methods.

Participants in the Sampling Program enroll in these three courses and are considered Fellows in the Program. The methods and the analysis courses may be taken as stand-alone courses, but participation in the sampling workshop is reserved for Sampling Program Fellows.

The online application is now open at http://www.si.isr.umich.edu, and includes an optional application for limited scholarship assistance through the Leslie Kish International Fellowship.

For more details about course descriptions, dates, times, and other relevant information, please visit http://www.si.isr.umich.edu/spss. You can also call (877) 880-9389 or email isr-summer@umich.edu for more information.

Remember the principle of statistical design: To understand the past, you must first know the future.