TY - JOUR
AU - 
AB - The hierarchical linear model (HLM) is the primary tool of multilevel analysis, a set of techniques for examining data with nested sources of variability. The concept of R from classical multiple regression analysis cannot be applied directly to HLMs without certain undesirable results. However, multilevel analogues have been formulated. The goal here is to demonstrate a SAS macro that will calculate estimates of these quantities for a two-level HLM that has been t with SAS's linear mixed modeling procedure, PROC MIXED. Keywords : hierarchical linear model, PROC MIXED, R-squared, SAS. 1. Introduction 1.1. Multilevel analysis Multilevel analysis is a set of statistical techniques for examining data with sources of vari- ability that are nested within one another. Data that possess such a structure arise frequently in practice. The simplest and most common form is two-level data, in which \level-1 units", or \individuals", are nested within \level-2 units", or \groups". Some typical examples of this scheme include students nested within classes, employees nested within rms, and measure- ments nested within subjects (in the case of longitudinal data). Snijders and Bosker (1999) give two reasons why this type of data tends to appear. First, it is often the case that there 
TI - R-Squared Measures for Two-Level Hierarchical Linear Models UsingSAS
JF - Journal of Statistical Software
DO - 10.18637/jss.v032.c02
DA - 2010-01-01
UR - https://www.deepdyve.com/lp/unpaywall/r-squared-measures-for-two-level-hierarchical-linear-models-usingsas-wlv6Fcvvab
DP - DeepDyve
ER -