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.IDa factor with 23 levels, representing the 23 schools within which students are nested.
SESa numeric vector, representing the socio-economic status
mean.SESa numeric vector, representing the mean socio-economic status per school
homeworka 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
parenteda 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
ratioa numeric vector, representing the student-teacher ratio
perc.minora 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
matha numeric vector, representing the number of correct answers on a mathematics test
sexa factor with levels Male and Female
racea factor with levels Asian, Hispanic, Black, White, and American Indian
school.typea factor representing the school type, with levels Public school, Catholic school, Private, other religious affiliation, and Private, no religious affiliation
structurea numeric vector representing the degree to which the classroom environment is structured. High values represent higher levels of (accurate) classroom environment structure
school.sizea factor representing the total school enrollment, with levels 1-199 Students, 200-399, 400-599, 600-799, 800-999, 1000-1199, and 1200+
urbana factor with levels Urban, Suburban, and Rural
regiona 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.
Kreft, I. and De Leeuw, J. (1998). Introducing Multilevel Modeling. Sage Publications.
data(school23)
model <- lmer(math ~ structure + (1 | school.ID), data=school23)
summary(model)
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