star: Student Teacher Achievement Ratio (STAR) project data

Description Usage Format Details Source Examples

Description

Data from Tennessee's Student Teacher Achievement Ratio (STAR) project which was a large-scale, four-year study of the effect of reduced class size.

Usage

1

Format

A data frame with 26796 observations on the following 18 variables.

id

a factor - student id number

sch

a factor - school id number

gr

grade - an ordered factor with levels K < 1 < 2 < 3

cltype

class type - a factor with levels small, reg and reg+A. The last level indicates a regular class size with a teachers aide.

hdeg

highest degree obtained by the teacher - an ordered factor with levels ASSOC < BS/BA < MS/MA/MEd < MA+ < Ed.S < Ed.D/Ph.D

clad

career ladder position of the teacher - a factor with levels NOT APPR PROB PEND 1 2 3

exp

a numeric vector - the total number of years of experience of the teacher

trace

teacher's race - a factor with levels W, B, A, H, I and O representing white, black, Asian, Hispanic, Indian (Native American) and other

read

the student's total reading scaled score

math

the student's total math scaled score

ses

socioeconomic status - a factor with levels F and N representing eligible for free lunches or not eligible

schtype

school type - a factor with levels inner, suburb, rural and urban

sx

student's sex - a factor with levels M F

eth

student's ethnicity - a factor with the same levels as trace

birthq

student's birth quarter - an ordered factor with levels 1977:1 < ... < 1982:2

birthy

student's birth year - an ordered factor with levels 1977:1982

yrs

number of years of schooling for the student - a numeric version of the grade gr with Kindergarten represented as 0. This variable was generated from gr and does not allow for a student being retained.

tch

a factor - teacher id number

Details

Details of the original data source and the process of conversion to this representation are given in the vignette.

Source

http://www.heros-inc.org/data.htm

Examples

1

Example output

Loading required package: lme4
Loading required package: Matrix
'data.frame':	26796 obs. of  18 variables:
 $ id     : Factor w/ 11598 levels "100017","100028",..: 1 2 3 3 3 4 5 5 6 6 ...
 $ sch    : Factor w/ 80 levels "1","2","3","4",..: 28 52 41 41 41 64 40 40 22 22 ...
 $ gr     : Ord.factor w/ 4 levels "K"<"1"<"2"<"3": 1 1 2 3 4 1 1 2 1 2 ...
 $ cltype : Factor w/ 3 levels "small","reg",..: 1 2 1 1 1 1 2 3 1 1 ...
 $ hdeg   : Ord.factor w/ 6 levels "ASSOC"<"BS/BA"<..: 2 3 2 3 2 2 2 2 2 3 ...
 $ clad   : Factor w/ 7 levels "NOT","APPR","PROB",..: 5 5 5 2 5 5 2 5 3 5 ...
 $ exp    : int  3 12 20 15 5 19 2 5 9 9 ...
 $ trace  : Factor w/ 6 levels "W","B","A","H",..: 2 1 1 2 1 1 1 1 2 2 ...
 $ read   : int  476 410 507 575 610 430 495 629 418 524 ...
 $ math   : int  602 444 572 620 655 484 576 592 489 567 ...
 $ ses    : Factor w/ 2 levels "F","N": 1 2 2 2 2 2 1 1 1 1 ...
 $ schtype: Factor w/ 4 levels "inner","suburb",..: 1 2 2 2 2 3 3 3 1 1 ...
 $ sx     : Factor w/ 2 levels "M","F": 2 2 1 1 1 2 2 2 2 2 ...
 $ eth    : Factor w/ 6 levels "W","B","A","H",..: 2 1 1 1 1 1 1 1 2 2 ...
 $ birthq : Ord.factor w/ 25 levels "1977:1"<"1977:2"<..: 14 15 15 15 15 14 12 12 14 14 ...
 $ birthy : Ord.factor w/ 6 levels "1977"<"1978"<..: 4 4 4 4 4 4 3 3 4 4 ...
 $ yrs    : num  0 0 1 2 3 0 0 1 0 1 ...
 $ tch    : Factor w/ 1387 levels "1","2","3","4",..: 478 893 698 701 706 1102 679 684 351 359 ...

mlmRev documentation built on May 31, 2017, 3:29 a.m.