Description Usage Format Details Source References Examples

The Academic Performance Index is computed for all California schools based on standardised testing of students. The data sets contain information for all schools with at least 100 students and for various probability samples of the data.

1 |

The full population data in `apipop`

are a data frame with 6194 observations on the following 37 variables.

- cds
Unique identifier

- stype
Elementary/Middle/High School

- name
School name (15 characters)

- sname
School name (40 characters)

- snum
School number

- dname
District name

- dnum
District number

- cname
County name

- cnum
County number

- flag
reason for missing data

- pcttest
percentage of students tested

- api00
API in 2000

- api99
API in 1999

- target
target for change in API

- growth
Change in API

- sch.wide
Met school-wide growth target?

- comp.imp
Met Comparable Improvement target

- both
Met both targets

- awards
Eligible for awards program

- meals
Percentage of students eligible for subsidized meals

- ell
‘English Language Learners’ (percent)

- yr.rnd
Year-round school

- mobility
percentage of students for whom this is the first year at the school

- acs.k3
average class size years K-3

- acs.46
average class size years 4-6

- acs.core
Number of core academic courses

- pct.resp
percent where parental education level is known

- not.hsg
percent parents not high-school graduates

- hsg
percent parents who are high-school graduates

- some.col
percent parents with some college

- col.grad
percent parents with college degree

- grad.sch
percent parents with postgraduate education

- avg.ed
average parental education level

- full
percent fully qualified teachers

- emer
percent teachers with emergency qualifications

- enroll
number of students enrolled

- api.stu
number of students tested.

The other data sets contain additional variables `pw`

for
sampling weights and `fpc`

to compute finite population
corrections to variance.

`apipop`

is the entire population, `apisrs`

is a simple random sample,
`apiclus1`

is a cluster sample of school districts, `apistrat`

is
a sample stratified by `stype`

, and `apiclus2`

is a two-stage
cluster sample of schools within districts. The sampling weights in
`apiclus1`

are incorrect (the weight should be 757/15) but are as
obtained from UCLA.

Data were obtained from the survey sampling help pages of UCLA Academic Technology Services; these pages are no longer on line.

The API program and original data files are at http://api.cde.ca.gov/

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | ```
library(survey)
data(api)
mean(apipop$api00)
sum(apipop$enroll, na.rm=TRUE)
#stratified sample
dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)
summary(dstrat)
svymean(~api00, dstrat)
svytotal(~enroll, dstrat, na.rm=TRUE)
# one-stage cluster sample
dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc)
summary(dclus1)
svymean(~api00, dclus1)
svytotal(~enroll, dclus1, na.rm=TRUE)
# two-stage cluster sample
dclus2<-svydesign(id=~dnum+snum, fpc=~fpc1+fpc2, data=apiclus2)
summary(dclus2)
svymean(~api00, dclus2)
svytotal(~enroll, dclus2, na.rm=TRUE)
# two-stage `with replacement'
dclus2wr<-svydesign(id=~dnum+snum, weights=~pw, data=apiclus2)
summary(dclus2wr)
svymean(~api00, dclus2wr)
svytotal(~enroll, dclus2wr, na.rm=TRUE)
# convert to replicate weights
rclus1<-as.svrepdesign(dclus1)
summary(rclus1)
svymean(~api00, rclus1)
svytotal(~enroll, rclus1, na.rm=TRUE)
# post-stratify on school type
pop.types<-xtabs(~stype, data=apipop)
rclus1p<-postStratify(rclus1, ~stype, pop.types)
dclus1p<-postStratify(dclus1, ~stype, pop.types)
summary(dclus1p)
summary(rclus1p)
svymean(~api00, dclus1p)
svytotal(~enroll, dclus1p, na.rm=TRUE)
svymean(~api00, rclus1p)
svytotal(~enroll, rclus1p, na.rm=TRUE)
``` |

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