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Last data update: 2014.03.03 |
Project STAR: Student-Teacher Achievement RatioDescriptionThe Project STAR public access data set, assessing the effect of reducing class size on test scores in the early grades. Usagedata("STAR")
FormatA data frame containing 11,598 observations on 47 variables.
DetailsProject 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 at http://www.heros-inc.org/star.htm. The data is provided in wide format. Reshaping it into long format
is illustrated below. Note that the levels of the SourceOnline complements to Stock and Watson (2007). http://wps.aw.com/aw_stock_ie_2/ ReferencesStock, J.H. and Watson, M.W. (2007). Introduction to Econometrics, 2nd ed. Boston: Addison Wesley. See Also
Examples
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)
Results
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> library(AER)
Loading required package: car
Loading required package: lmtest
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
Loading required package: sandwich
Loading required package: survival
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/AER/STAR.Rd_%03d_medium.png", width=480, height=480)
> ### Name: STAR
> ### Title: Project STAR: Student-Teacher Achievement Ratio
> ### Aliases: STAR
> ### Keywords: datasets
>
> ### ** Examples
>
> 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)
t test of coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1228.50636 1.67959 731.4322 < 2.2e-16 ***
star3small 15.58660 2.39544 6.5068 8.303e-11 ***
star3regular+aide -0.29094 2.27214 -0.1280 0.8981
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> 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)
t test of coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 904.72124 2.22157 407.2433 < 2.2e-16 ***
starksmall 14.00613 2.44620 5.7257 1.082e-08 ***
starkregular+aide -0.60058 2.25352 -0.2665 0.7899
experiencek 1.46903 0.16923 8.6808 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>
> ## 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)
t test of coefficients:
Estimate Std. Error t value Pr(>|t|)
gradekindergarten 918.04289 1.63297 562.1930 < 2.2e-16 ***
grade1st 1039.39259 1.78415 582.5691 < 2.2e-16 ***
grade2nd 1157.80664 1.81463 638.0403 < 2.2e-16 ***
grade3rd 1228.50636 1.67959 731.4322 < 2.2e-16 ***
gradekindergarten:starsmall 13.89899 2.45346 5.6651 1.486e-08 ***
grade1st:starsmall 29.78077 2.83048 10.5215 < 2.2e-16 ***
grade2nd:starsmall 19.39437 2.71099 7.1540 8.671e-13 ***
grade3rd:starsmall 15.58660 2.39544 6.5068 7.828e-11 ***
gradekindergarten:starregular+aide 0.31394 2.27039 0.1383 0.8900
grade1st:starregular+aide 11.95872 2.65139 4.5104 6.502e-06 ***
grade2nd:starregular+aide 3.47914 2.54409 1.3675 0.1715
grade3rd:starregular+aide -0.29094 2.27214 -0.1280 0.8981
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>
> ## visualization
> library("lattice")
> bwplot(I(read + math) ~ star | grade, data = star)
>
>
>
>
>
> dev.off()
null device
1
>
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