egsingle: US Sustaining Effects study

In mlmRev: Examples from Multilevel Modelling Software Review

Description

A subset of the mathematics scores from the U.S. Sustaining Effects Study. The subset consists of information on 1721 students from 60 schools

Usage

data(egsingle)

Format

A data frame with 7230 observations on the following 12 variables.

schoolid

a factor of school identifiers

childid

a factor of student identifiers

year

a numeric vector indicating the year of the test

grade

a numeric vector indicating the student's grade

math

a numeric vector of test scores on the IRT scale score metric

retained

a factor with levels 0 1 indicating if the student has been retained in a grade.

female

a factor with levels Female Male indicating the student's sex

black

a factor with levels 0 1 indicating if the student is Black

hispanic

a factor with levels 0 1 indicating if the student is Hispanic

size

a numeric vector indicating the number of students enrolled in the school

lowinc

a numeric vector giving the percentage of low-income students in the school

mobility

a numeric vector

Source

These data are distributed with the HLM software package (Bryk, Raudenbush and Congdon, 1996). Conversion to the R format is described in Doran and Lockwood (2004).

References

Doran, Harold C. and Lockwood, J.R. (2004), Fitting value-added models in R, (submitted).

Examples

str(egsingle)
(fm1 <- lmer(math~year*size+female+(1|childid)+(1|schoolid), egsingle))

Example output

Loading required package: lme4
Loading required package: Matrix
'data.frame':	7230 obs. of  12 variables:
 $ schoolid: Factor w/ 60 levels "2020","2040",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ childid : Factor w/ 1721 levels "101480302","173559292",..: 244 244 244 248 248 248 248 248 253 253 ...
 $ year    : num  0.5 1.5 2.5 -1.5 -0.5 0.5 1.5 2.5 -1.5 -0.5 ...
 $ grade   : num  2 3 4 0 1 2 3 4 0 1 ...
 $ math    : num  1.146 1.134 2.3 -1.303 0.439 ...
 $ retained: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
 $ female  : Factor w/ 2 levels "Female","Male": 1 1 1 1 1 1 1 1 1 1 ...
 $ black   : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
 $ hispanic: Factor w/ 2 levels "0","1": 2 2 2 1 1 1 1 1 2 2 ...
 $ size    : num  380 380 380 380 380 380 380 380 380 380 ...
 $ lowinc  : num  40.3 40.3 40.3 40.3 40.3 40.3 40.3 40.3 40.3 40.3 ...
 $ mobility: num  12.5 12.5 12.5 12.5 12.5 12.5 12.5 12.5 12.5 12.5 ...
Linear mixed model fit by REML ['lmerMod']
Formula: math ~ year * size + female + (1 | childid) + (1 | schoolid)
   Data: egsingle
REML criterion at convergence: 16784.78
Random effects:
 Groups   Name        Std.Dev.
 childid  (Intercept) 0.8181  
 schoolid (Intercept) 0.4235  
 Residual             0.5886  
Number of obs: 7230, groups:  childid, 1721; schoolid, 60
Fixed Effects:
(Intercept)         year         size   femaleMale    year:size  
 -5.980e-01    7.917e-01   -2.738e-04   -4.653e-03   -6.067e-05  
fit warnings:
Some predictor variables are on very different scales: consider rescaling
Warning message:
Some predictor variables are on very different scales: consider rescaling 

mlmRev documentation built on April 5, 2020, 9:07 a.m.