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Schooldata Sites

Source: R/schoolsites.R
schoolsites.Rd

Descriptors for the sites of the schooldata dataset, from Charnes et al. (1981). The study was designed to compare schools using Program Follow Through (PFT) management methods of taking actions to achieve goals with those of Non Follow Through (NFT). Observations 1:49 came from PFT sites and 50:70 from NFT sites. This dataset gives other descriptors for the sites, from their Exhibit C.

Usage

data("schoolsites")

Format

A data frame with 70 observations on the following 7 variables.

site

site number, a numeric vector

type

program type, a factor with levels PFT ("Program Follow Through") and NFT ("Non Follow Through")

model

education style model, a factor with levels BA, Bank Street, California Process, Cognitive Curriculum, DIM, EDC, Home-School, ILM, Parent Education, Responsive Education, SEDL, TEEM

site_name

location of site, a character vector

region

US region, a factor with levels NC, NE, S, W

city_size

city size, an ordered factor with levels Rural < Small < Medium < Large

student_pop

size of the student population, a numeric vector

Source

A. Charnes, W.W. Cooper and E. Rhodes (1981). Evaluating Program and Managerial Efficiency: An Application of Data Envelopment Analysis to Program Follow Through. Management Science, 27, 668-697, Exhibit C.

See also

schooldata

Examples

data(schoolsites)
str(schoolsites)
#> 'data.frame':	70 obs. of  7 variables:
#>  $ site       : int  1 2 3 4 5 6 7 8 9 10 ...
#>  $ type       : Factor w/ 2 levels "PFT","NFT": 1 1 1 1 1 1 1 1 1 1 ...
#>  $ model      : Factor w/ 12 levels "BA","Bank Street",..: 10 10 10 10 10 10 10 12 12 12 ...
#>  $ site_name  : chr  "Berkeley, CA" "Buffalo, NY" "Duluth, MN" "Fresno, CA" ...
#>  $ region     : Factor w/ 4 levels "NC","NE","S",..: 4 2 1 4 2 4 4 3 2 1 ...
#>  $ city_size  : Ord.factor w/ 4 levels "Rural"<"Small"<..: 3 4 3 3 1 3 3 4 2 3 ...
#>  $ student_pop: int  99 77 77 48 14 36 51 99 80 96 ...
schools <- cbind(schooldata, schoolsites)
schools.mod <- lm(cbind(reading, mathematics, selfesteem) ~
                    education + occupation + visit + counseling + teacher +
                    type + region, data = schools)
car::Anova(schools.mod)
#> 
#> Type II MANOVA Tests: Pillai test statistic
#>            Df test stat approx F num Df den Df    Pr(>F)    
#> education   1   0.37965  11.8320      3     58 3.773e-06 ***
#> occupation  1   0.53605  22.3376      3     58 9.720e-10 ***
#> visit       1   0.26933   7.1264      3     58 0.0003721 ***
#> counseling  1   0.07204   1.5008      3     58 0.2238866    
#> teacher     1   0.01106   0.2163      3     58 0.8847243    
#> type        1   0.10345   2.2307      3     58 0.0942228 .  
#> region      3   0.26764   1.9591      9    180 0.0465145 *  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1