

Study with the several resources on Docsity
Earn points by helping other students or get them with a premium plan
Prepare for your exams
Study with the several resources on Docsity
Earn points to download
Earn points by helping other students or get them with a premium plan
Community
Ask the community for help and clear up your study doubts
Discover the best universities in your country according to Docsity users
Free resources
Download our free guides on studying techniques, anxiety management strategies, and thesis advice from Docsity tutors
An overview of different approaches to measuring the proportion of variance accounted for in multilevel regression models. The author compares the methods proposed by snijders and bosker, xu, and nakagawa and schielzeth using the bryk & raudenbush hsb data. The document also includes r code for computing the xu pseudo-r-square measure.
What you will learn
Typology: Slides
1 / 3
This page cannot be seen from the preview
Don't miss anything!
Psy 526/626 Multilevel Regression, Spring 2019 1
Final estimation of fixed effects:
Fixed Effect Coefficient Standard error
t -ratio Approx. d.f.
p -value
For INTRCPT1, β 0 INTRCPT2, γ 00 12.636972 0.244412 51.704 159 <0.
Final estimation of variance components
Random Effect
Standard Deviation
Variance Component
d.f. χ^2 p -value
INTRCPT1, u 0 2.93501 8.61431 159 1660.23259 <0. level-1, r 6.25686 39.
Final estimation of fixed effects:
Fixed Effect Coefficient Standard error
t -ratio Approx. d.f.
p -value
For INTRCPT1, β 0 INTRCPT2, γ 00 12.109382 0.198644 60.960 157 <0. SECTOR, γ 01 1.224539 0.306080 4.001 157 <0. MEANSES, γ 02 3.145129 0.384611 8.177 157 <0. For SES slope, β 1 INTRCPT2, γ 10 2.191168 0.108673 20.163 7024 <0.
Final estimation of variance components
Random Effect Standard Deviation
Variance Component
d.f. χ^2 p -value
INTRCPT1, u 0 1.53905 2.36866 157 600.10638 <0. level-1, r 6.08465 37.
( ) ( )
( ) ( )
0
0
2 2 2 1 2 2
σ τ
σ τ
= −
= − = − =
Psy 526/626 Multilevel Regression, Spring 2019 2
2
2 2 2 0 (^2 2 ) 0 37.02 (^) 2. 1 45 39.15 (^) 8. 45 .82 2. 1 .87 8.
1 1 .34.
= −
= −
= − = − =
0
2 2
library(sjstats) #used for r2 computation below
#get empty (or null) model modeln <- lmer (mathach ~ 1 + (1 | schoolid), data=mydata,REML=FALSE) summary(modeln) [output omitted] #full model with ML modelf <- lmer (mathach ~ ses + sector + meanses + (1 | schoolid), data=mydata,REML=FALSE) summary(modelf) [output omitted] #get R-square approximations--use FIML estimation instead of REML (REML = FALSE) as in model #compute the Xu (2003) measure (given as R-squared in output) #r2 function requires sjstats package, loaded before lmer run #note: Omega-squared in output matches Snijders & Bosker R-sq r2(modelf, modeln) R-squared (tau-00): 0. R-squared (tau-11): NA Omega-squared: 0. R-squared: 0.