Thomas Lumley • 1/18/2019

Another way to see why mixed models in survey data are hard:

Read Original

This technical article examines why fitting linear mixed models (like random intercept models) to survey data with multi-stage sampling (e.g., schools and students) is problematic. It details how standard weighting approaches using inverse sampling probabilities for different model components fail, leading to biased variance estimates, and discusses the conceptual clash between model-based and design-based inference.

0 comments
#Linear Regression #R #Statistical Computing
Another way to see why mixed models in survey data are hard:

Comments

No comments yet

Be the first to share your thoughts!

Browser Extension

Get instant access to AllDevBlogs from your browser

Top of the Week

1
React, useEffect, and Stale Closures: How to Avoid Them with Dependencies and useRef
Jivbcoop • 4 votes
2
Better react-hook-form Smart Form Components
Maarten Hus • 2 votes
3
AGI, ASI, A*I – Do we have all we need to get there?
John D. Cook • 1 votes
4
Quoting Thariq Shihipar
Simon Willison • 1 votes
5
Dew Drop – January 15, 2026 (#4583)
Alvin Ashcraft • 1 votes
6
Using Browser Apis In React Practical Guide
Jivbcoop • 1 votes