Modern inference methods for non-probability samples with R

Author

Łukasz Chrostowski, Maciej Beręsewicz

1 Welcome!

Welcome to the book on Modern inference methods for non-probability samples in R! In this comprehensive guide, we will delve into the details of statistical inference, focusing specifically on the unique challenges and techniques associated with non-probability samples.

Throughout this course, you will embark on a journey that combines both theoretical considerations and hands-on practicality. Whether you’re an aspiring statistician, a data scientist, or a researcher in any field, this material will equip you with the knowledge and tools needed to navigate the intricacies of non-probability samples and extract meaningful insights from your data.

Here’s a glimpse of what you can expect to know:

  1. Understanding Non-probability Samples: Gain a deep understanding of what nonprobability samples are, their characteristics, and the contexts in which they are commonly used.

  2. Challenges and Biases: Explore the inherent challenges and biases associated with non-probability samples, including selection bias, coverage bias, and non-response bias.

  3. Statistical Inference Techniques: Discover specialized statistical methods designed to address the unique characteristics of non-probability samples, including propensity score weighting and imputation techniques.

  4. Hands-on R Programming: Introduction of R nonprobsvy package for inference with non-probability sample.

Have a great read!