Weights

Which weights to use with multilevel models? A common question with the use of large-scale assessments (LSAs) is related to the use of weights. Another issue is how to specify these weights properly. Software such as SAS and Mplus, when specifying weights at two levels, require the use of conditional weights at level 1 if the level-2 weight is specified (or you can just use the level-2 weights alone; see Mang et al.

Years ago I had written a post on using multiple imputation, weights, and accounting for clustering using R. However, the process was actually quite cumbersome and now in 2024, there are more straightforward ways of handling this. 1. Load in the required packges library(dplyr) #for basic data management library(tidyr) #converting wide to tall library(estimatr) #estimating models with robust SEs library(mitml) #for imputation and analyzing MI datasets library(MLMusingR) #contains the sample dataset library(mice) #for carrying out the analysis with MI data library(modelsummary) #outputting the results nicely library(survey) #alternative (classic) way 2.

In class, I’ve talked about using weights in large scale assessments. I’ve provided a bit of intuition about using weights and why they are important. Here’s some R syntax to go along with the example I discussed. Imagine there are two schools in one school district. You are asked what is the average score on some measure of students in the district. There are only two schools (school A and B) and their size varies (School A = 100, School B = 1,000).