Short Courses
Short courses will be given on Monday, June 17, 2024.
All courses are half-day courses. Light breakfast, lunch, and morning or afternoon refreshments are included in the fee.
Morning Half-Day Courses
8:30 a.m. – 12:30 p.m.
SC01: Navigating the Privacy-Utility Tradeoff: An Introduction to Data Privacy Techniques (Introductory Level)
Instructor: Aaron R. Williams, Urban Institute
This course will provide an overview of current data privacy methodology, focusing on the generation of synthetic data and application of differentially private methods. Through examinations of case studies, attendees will learn to apply data privacy techniques and evaluate the resulting disclosure risk and data utility.
SC02: Electronic Questionnaire Design for Businesses (Introductory Level)
Instructor: Ger Snijkers, Statistics Netherlands
This course aims to introduce the design principles for electronic business questionnaires. We will discuss guidelines for electronic business questionnaire design and evaluate their pragmatic application in a (re)design of (paper) questionnaire(s). To illustrate the guidelines, we will discuss examples of electronic business questionnaires and examples from Statistics Netherlands. We will work toward a two-way dialogue approach, so participants are invited to discuss their own questionnaires.
Afternoon Half-Day Courses
1:30 p.m. – 5:30 p.m.
SC03: Seasonal Adjustment and Time Series Analysis (Introductory Level)
Instructors: James A. Livsey, US Census Bureau, and Anna Smyk, INSEE
This course aims to offer participants an introduction to time series seasonal adjustment. Its main objectives include introducing foundational concepts in time series, exploring the nuances of seasonal adjustment methodology, and providing hands-on experience implementing these techniques through the R programming language. The course is designed to be accessible to beginners seeking an introduction to seasonal adjustment while also catering to experienced practitioners aiming to deepen their understanding of advanced time series techniques such as seasonal adjustment of high-frequency (infra-monthly) data.
SC04: Data Science in Official Statistics (Introductory Level)
Instructors: Kelly McConville, Harvard University, and Wesley Yung, Statistics Canada
National statistical offices are actively integrating data science methods into many aspects of producing official statistics, including data collection, editing and imputation, and survey estimation. As technological and statistical advances provide new data sources and modeling techniques, procedures must adapt to accommodate them. This short course will provide participants with examples of how data science methods are used in producing official statistics and the challenges faced. It will also introduce participants to a model-assisted approach for incorporating machine learning into survey estimation. The machine learning models will include generalized linear models, regularized (elastic net) regression, and regression trees. The course will also include demonstrations of how to fit these estimators using the statistical software R. R Markdown files with the relevant code will be provided so participants can actively follow along with the demonstrations. Prior R experience is encouraged but not required.
SC05: Multiple Imputation (Introductory Level)
Instructor: Jörg Drechsler, IAB
This course aims to illustrate the usefulness (and limitations) of multiple imputation and enable students to develop sensible imputation strategies when dealing with item nonresponse in large-scale business surveys. While the course offers a general introduction to the topic, specific attention will be given to imputation problems encountered in the context of business data.
