school23: Math test performance in 23 schools
In influence.ME: Tools for Detecting Influential Data in Mixed Effects Models

Description Format Details Source References Examples

The school23 data contains information on students' performance on a math test, as well as several explanatory variables. These data are subset of the NELS-88 data (National Education Longitudinal Study of 1988). Both a selected number of variables and a selected number of observations are given here.

A data frame with 519 observations on the following 15 variables.

school.ID: a factor with 23 levels, representing the 23 schools within which students are nested.
SES: a numeric vector, representing the socio-economic status
mean.SES: a numeric vector, representing the mean socio-economic status per school
homework: a factor representing the time spent on math homework each week, with levels None, Less than 1 hour, 1 hour, 2 hours, 3 hours, 4-6 hours, 7-9 hours, and 10 or more
parented: a factor representing the parents' highest education level, with levels Dod not finish H.S., H.S. grad or GED, GT H.S. and LT 4yr degree, College graduate, M.A. or equivalent, and Ph.D., M.D., other
ratio: a numeric vector, representing the student-teacher ratio
perc.minor: a factor representing the percent minority in school, with levels None, 1-5, 6-10, 11-20, 21-40, 41-60, 61-90, and 91-100
math: a numeric vector, representing the number of correct answers on a mathematics test
sex: a factor with levels Male and Female
race: a factor with levels Asian, Hispanic, Black, White, and American Indian
school.type: a factor representing the school type, with levels Public school, Catholic school, Private, other religious affiliation, and Private, no religious affiliation
structure: a numeric vector representing the degree to which the classroom environment is structured. High values represent higher levels of (accurate) classroom environment structure
school.size: a factor representing the total school enrollment, with levels 1-199 Students, 200-399, 400-599, 600-799, 800-999, 1000-1199, and 1200+
urban: a factor with levels Urban, Suburban, and Rural
region: a factor with levels Northeast, North Central, South, and West

Labels for the factors were found in an appendix in Kreft \& De Leeuw (1998). All labels were designated, although in some cases not all possible values are represented in the variable (i.e. region). This is probably due to the fact that this is only a subsample from the full NELS-88 data.

Also, some of the variable names were changed.

These data are used in the examples given in Kreft \& De Leeuw (1998). Both the examples and the data are publicly available from the internet: http://www.ats.ucla.edu/stat/examples/imm/. Data reproduced with permission from Jan de Leeuw.

Kreft, I. and De Leeuw, J. (1998). Introducing Multilevel Modeling. Sage Publications.

## Not run: 
data(school23)
model <- lmer(math ~ structure + (1 | school.ID), data=school23)
summary(model)

## End(Not run)

Loading required package: lme4
Loading required package: Matrix

Attaching package: 'influence.ME'

The following object is masked from 'package:stats':

    influence

Linear mixed model fit by REML ['lmerMod']
Formula: math ~ structure + (1 | school.ID)
   Data: school23

REML criterion at convergence: 3793.6

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.67202 -0.71406 -0.01811  0.73822  2.66971 

Random effects:
 Groups    Name        Variance Std.Dev.
 school.ID (Intercept) 23.88    4.887   
 Residual              81.27    9.015   
Number of obs: 519, groups:  school.ID, 23

Fixed effects:
            Estimate Std. Error t value
(Intercept)   60.002      5.853  10.251
structure     -2.343      1.456  -1.609

Correlation of Fixed Effects:
          (Intr)
structure -0.982