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.

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```
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
```

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