AER (version 1.2-9)

STAR: Project STAR: Student-Teacher Achievement Ratio

Description

The Project STAR public access data set, assessing the effect of reducing class size on test scores in the early grades.

Usage

data("STAR")

Arguments

Format

A data frame containing 11,598 observations on 47 variables.

gender

factor indicating student's gender.

ethnicity

factor indicating student's ethnicity with levels "cauc" (Caucasian), "afam" (African-American), "asian" (Asian), "hispanic" (Hispanic), "amindian" (American-Indian) or "other".

birth

student's birth quarter (of class yearqtr).

stark

factor indicating the STAR class type in kindergarten: regular, small, or regular-with-aide. NA indicates that no STAR class was attended.

star1

factor indicating the STAR class type in 1st grade: regular, small, or regular-with-aide. NA indicates that no STAR class was attended.

star2

factor indicating the STAR class type in 2nd grade: regular, small, or regular-with-aide. NA indicates that no STAR class was attended.

star3

factor indicating the STAR class type in 3rd grade: regular, small, or regular-with-aide. NA indicates that no STAR class was attended.

readk

total reading scaled score in kindergarten.

read1

total reading scaled score in 1st grade.

read2

total reading scaled score in 2nd grade.

read3

total reading scaled score in 3rd grade.

mathk

total math scaled score in kindergarten.

math1

total math scaled score in 1st grade.

math2

total math scaled score in 2nd grade.

math3

total math scaled score in 3rd grade.

lunchk

factor indicating whether the student qualified for free lunch in kindergarten.

lunch1

factor indicating whether the student qualified for free lunch in 1st grade.

lunch2

factor indicating whether the student qualified for free lunch in 2nd grade.

lunch3

factor indicating whether the student qualified for free lunch in 3rd grade.

schoolk

factor indicating school type in kindergarten: "inner-city", "suburban", "rural" or "urban".

school1

factor indicating school type in 1st grade: "inner-city", "suburban", "rural" or "urban".

school2

factor indicating school type in 2nd grade: "inner-city", "suburban", "rural" or "urban".

school3

factor indicating school type in 3rd grade: "inner-city", "suburban", "rural" or "urban".

degreek

factor indicating highest degree of kindergarten teacher: "bachelor", "master", "specialist", or "master+".

degree1

factor indicating highest degree of 1st grade teacher: "bachelor", "master", "specialist", or "phd".

degree2

factor indicating highest degree of 2nd grade teacher: "bachelor", "master", "specialist", or "phd".

degree3

factor indicating highest degree of 3rd grade teacher: "bachelor", "master", "specialist", or "phd".

ladderk

factor indicating teacher's career ladder level in kindergarten: "level1", "level2", "level3", "apprentice", "probation" or "pending".

ladder1

factor indicating teacher's career ladder level in 1st grade: "level1", "level2", "level3", "apprentice", "probation" or "noladder".

ladder2

factor indicating teacher's career ladder level in 2nd grade: "level1", "level2", "level3", "apprentice", "probation" or "noladder".

ladder3

factor indicating teacher's career ladder level in 3rd grade: "level1", "level2", "level3", "apprentice", "probation" or "noladder".

experiencek

years of teacher's total teaching experience in kindergarten.

experience1

years of teacher's total teaching experience in 1st grade.

experience2

years of teacher's total teaching experience in 2nd grade.

experience3

years of teacher's total teaching experience in 3rd grade.

tethnicityk

factor indicating teacher's ethnicity in kindergarten with levels "cauc" (Caucasian) or "afam" (African-American).

tethnicity1

factor indicating teacher's ethnicity in 1st grade with levels "cauc" (Caucasian) or "afam" (African-American).

tethnicity2

factor indicating teacher's ethnicity in 2nd grade with levels "cauc" (Caucasian) or "afam" (African-American).

tethnicity3

factor indicating teacher's ethnicity in 3rd grade with levels "cauc" (Caucasian), "afam" (African-American), or "asian" (Asian).

systemk

factor indicating school system ID in kindergarten.

system1

factor indicating school system ID in 1st grade.

system2

factor indicating school system ID in 2nd grade.

system3

factor indicating school system ID in 3rd grade.

schoolidk

factor indicating school ID in kindergarten.

schoolid1

factor indicating school ID in 1st grade.

schoolid2

factor indicating school ID in 2nd grade.

schoolid3

factor indicating school ID in 3rd grade.

Details

Project STAR (Student/Teacher Achievement Ratio) was a four-year longitudinal class-size study funded by the Tennessee General Assembly and conducted in the late 1980s by the State Department of Education. Over 7,000 students in 79 schools were randomly assigned into one of three interventions: small class (13 to 17 students per teacher), regular class (22 to 25 students per teacher), and regular-with-aide class (22 to 25 students with a full-time teacher's aide). Classroom teachers were also randomly assigned to the classes they would teach. The interventions were initiated as the students entered school in kindergarten and continued through third grade.

The Project STAR public access data set contains data on test scores, treatment groups, and student and teacher characteristics for the four years of the experiment, from academic year 1985--1986 to academic year 1988--1989. The test score data analyzed in this chapter are the sum of the scores on the math and reading portion of the Stanford Achievement Test.

Stock and Watson (2007) obtained the data set from the Project STAR Web site.

The data is provided in wide format. Reshaping it into long format is illustrated below. Note that the levels of the degree, ladder and tethnicity variables differ slightly between kindergarten and higher grades.

References

Stock, J.H. and Watson, M.W. (2007). Introduction to Econometrics, 2nd ed. Boston: Addison Wesley.

See Also

StockWatson2007

Examples

# NOT RUN {
data("STAR")

## Stock and Watson, p. 488
fmk <- lm(I(readk + mathk) ~ stark, data = STAR)
fm1 <- lm(I(read1 + math1) ~ star1, data = STAR)
fm2 <- lm(I(read2 + math2) ~ star2, data = STAR)
fm3 <- lm(I(read3 + math3) ~ star3, data = STAR)

coeftest(fm3, vcov = sandwich)
plot(I(read3 + math3) ~ star3, data = STAR)

## Stock and Watson, p. 489
fmke <- lm(I(readk + mathk) ~ stark + experiencek, data = STAR)
coeftest(fmke, vcov = sandwich)

## reshape data from wide into long format
## 1. variables and their levels
nam <- c("star", "read", "math", "lunch", "school", "degree", "ladder",
  "experience", "tethnicity", "system", "schoolid")
lev <- c("k", "1", "2", "3")
## 2. reshaping
star <- reshape(STAR, idvar = "id", ids = row.names(STAR),
  times = lev, timevar = "grade", direction = "long",
  varying = lapply(nam, function(x) paste(x, lev, sep = "")))
## 3. improve variable names and type
names(star)[5:15] <- nam
star$id <- factor(star$id)
star$grade <- factor(star$grade, levels = lev, labels = c("kindergarten", "1st", "2nd", "3rd"))
rm(nam, lev)

## fit a single model nested in grade (equivalent to fmk, fm1, fm2, fmk)
fm <- lm(I(read + math) ~ 0 + grade/star, data = star)
coeftest(fm, vcov = sandwich)

## visualization
library("lattice")
bwplot(I(read + math) ~ star | grade, data = star)
# }