The course is based on a total survey error perspective and discusses the major sources of survey error. Participants will be presented with tools for detection and adjustment of such errors. Analysis methods are introduced using R statistical software. Knowledge of R is beneficial but not required if you have worked with SPSS, Stata, SAS or other statistical software already. Topics include coverage errors, complex sampling, nonresponse adjustment, measurement error, and analysis of incomplete data. Special attention will be given to the analysis of complex surveys that include weighting, stratification and design effects.
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Changes in technology and society strongly influence modern survey research. This course covers the essentials of modern survey methodology, organized by the Department of Methodology and Statistics. Central to the course is survey quality and the reduction of Total Survey Error (coverage, sampling, nonresponse, including questionnaire and mode effects), while balancing logistics and survey costs. Best practice guidelines for surveys from design to implementation, analysis and reporting will be discussed.
This course in ‘advanced survey design’ takes students beyond the introductory courses and discusses the state of the art in both the design and the analysis of modern survey data, with a focus on new types of data. We present new ways to analyse modern surveys, including non-probability survey designs, smartphone data collection, digital trace data and data collected via apps. Course participants must be proficient working with the statistical software package R at the level of at least knowing Tidy and multivariate regression in R.